{"source":{"name":"The Evidence Hub - on the regulation of digital services","url":"https:\/\/evidencehub.net","license":"Creative Common CC-BY 4.0 International"},"data" : [
{"data": [{"data": [24.61, 22.6, 16.92, 11.94, 7.49, 6.91, 5.52, 2.75, 1.1, 0.16], "name": "Percent of online complaints"}], "_data": [["Type of Hate", "Percent of online complaints"], ["Racism", "24.61"], ["Antisemitism", "22.6"], ["Anti-Muslim hate", "16.92"], ["Anti-Refugee hate", "11.94"], ["Hate against non-religious people", "7.49"], ["Anti-Arab racism", "6.91"], ["Xenophobia", "5.52"], ["Homophobia ", "2.75"], ["Anti-Ziganism", "1.1"], ["Anti religious hate (anything but Islamophobia)", "0.16"]], "labels": {"name": "Type of Hate", "values": ["Racism", "Antisemitism", "Anti-Muslim hate", "Anti-Refugee hate", "Hate against non-religious people", "Anti-Arab racism", "Xenophobia", "Homophobia ", "Anti-Ziganism", "Anti religious hate (anything but Islamophobia)"]}, "metadata": {"link": "http://www.inach.net/the-state-of-cyber-hate/ ", "type": "Problem", "unit": "Percent of online complaints (%)", "year": "2018", "title": "Distribution of Online Complaints Per Hate Type", "topic": "Hate Speech", "method": "Data collection", "source": "Berecz, Tamás and Charlotte Devinat. The State of Cyber Hate (Brussels: International Network Against Cyber Hate, 2018)", "sub_topic": "Types of hate speech", "chart_number": "1", "geographical": "European Union"}, "description": "This chart shows the distribution of complaints about hate speech online based on data collected by International Network Against Cyberhate (INACH). INACH's 2018 report, \"The State of Cyber Hate,\" found that the most common types of hate speech reported in user complaints are Racism and Antisemitism. The least common types of hate speech are Anti-Ziganism and anti-religious hate for religions other than Islam."},
{"data": [{"data": [72.21, 21.59, 6.21], "name": "Per cent"}], "_data": [["Location of Complaint", "Per cent"], ["Websites (including comments)", "72.21"], ["Forums", "21.59"], ["Blogs", "6.21"]], "labels": {"name": "Location of Complaint", "values": ["Websites (including comments)", "Forums", "Blogs"]}, "metadata": {"link": "http://www.inach.net/the-state-of-cyber-hate/ ", "type": "Problem", "year": "2018", "title": "Distribution of Hate Speech Complains Across Web 1.0 Platforms (2018)", "topic": "Hate Speech", "method": "Data collection", "source": "Berecz, Tamás and Charlotte Devinat. The State of Cyber Hate (Brussels: International Network Against Cyber Hate, 2018)", "sub_topic": "Locations of hate speech", "chart_number": "2", "geographical": "European Union"}, "description": "The chart shows the distribution of hate speech complaints on Web 1.0 platforms based on data collected by the International Network Against Cyber Hate (INACH). INACH's 2018 report found that the majority (almost three quarters) of all complaints of hate speech were registered on websites (Web 1.0 platforms), followed by forums and blogs."},
{"data": [{"data": [40.08, 21.7, 21.2, 5.64, 3.59, 2.4, 2.29, 0.32, 0.32, 0.18, 0.14], "name": "Percent"}], "_data": [["Location of Complaint", "Percent"], ["Facebook", "40.08"], ["Twitter", "21.7"], ["YouTube", "21.2"], ["VK.com", "5.64"], ["Instagram", "3.59"], ["Google+", "2.4"], ["Telegram", "2.29"], ["Pintrest", "0.32"], ["Dailymotion.com", "0.32"], ["Tumblr", "0.18"], ["Vimeo", "0.14"]], "labels": {"name": "Location of Complaint", "values": ["Facebook", "Twitter", "YouTube", "VK.com", "Instagram", "Google+", "Telegram", "Pintrest", "Dailymotion.com", "Tumblr", "Vimeo"]}, "metadata": {"link": "http://www.inach.net/the-state-of-cyber-hate/ ", "type": "Problem", "unit": "Share of hate speech complaints", "year": "2018", "title": "Distribution of Hate Speech Complains Across Web 2.0 Platforms (2018)", "topic": "Hate Speech", "method": "Data collection", "source": "Berecz, Tamás and Charlotte Devinat. The State of Cyber Hate (Brussels: International Network Against Cyber Hate, 2018)", "sub_topic": "Locations of hate speech", "chart_number": "3", "geographical": "European Union"}, "description": "The chart presents the distribution of hate speech complaints on Web 2.0 platforms based on data collected by the International Network Against Cyber Hate. The 2018 report found that, when it comes to social media platforms, three major players give the biggest surface to cyber hate and extremist propaganda, accounting for more than 80% of all complaints of hate speech - Facebook (40%), Twitter (21.7%) and YouTube (21.2%)."},
{"data": [{"data": [29.28, 19.45, 15.85, 15.49, 5.82, 4.12, 2.62, 1.75, 1.67, 1.67, 1.57, 0.23, 0.23, 0.13, 0.1], "name": "Percent"}], "_data": [["Location of Complaint", "Percent"], ["Facebook", "29.28"], ["Websites (including comments)", "19.45"], ["Twitter", "15.85"], ["YouTube", "15.49"], ["Forums", "5.82"], ["VK.com", "4.12"], ["Instagram", "2.62"], ["Google+", "1.75"], ["Telegram", "1.67"], ["Blogs", "1.67"], ["Other social media sites ", "1.57"], ["Pinterest", "0.23"], ["Dailymotion.com", "0.23"], ["Tumblr", "0.13"], ["Vimeo", "0.1"]], "labels": {"name": "Location of Complaint", "values": ["Facebook", "Websites (including comments)", "Twitter", "YouTube", "Forums", "VK.com", "Instagram", "Google+", "Telegram", "Blogs", "Other social media sites ", "Pinterest", "Dailymotion.com", "Tumblr", "Vimeo"]}, "metadata": {"link": "http://www.inach.net/the-state-of-cyber-hate/ ", "type": "Problem", "unit": "Percent (%)", "year": "2018", "title": "Distribution of Hate Speech Complains Across All Platforms (2018)", "topic": "Hate Speech", "method": "Data collection", "source": "Berecz, Tamás and Charlotte Devinat. The State of Cyber Hate (Brussels: International Network Against Cyber Hate, 2018)", "sub_topic": "Locations of hate speech", "chart_number": "4", "geographical": "European Union"}, "description": "The chart shows the locations of complaints about hate speech on online platforms based on data collected by International Network Against Cyberhate (INACH). The INACH's report found that most instances of hate speech reported in user complaints are located on Facebook, traditional websites, Twitter, or YouTube."},
{"data": [{"data": [12.63, 17.91, 24.77, 25, 32.31, 33.33, 39.18, 41.86, 50, 51.95, 55.41, 55.56, 60.98, 91.67], "name": "Cases Not Removed"}, {"data": [87.37, 82.09, 75.23, 75, 67.69, 66.67, 60.82, 58.14, 50, 48.05, 44.59, 44.44, 39.02, 8.33], "name": "Cases Removed"}], "_data": [["Platform", "Cases Not Removed", "Cases Removed"], ["Instagram", "12.63", "87.37"], ["Forums", "17.91", "82.09"], ["YouTube", "24.77", "75.23"], ["Vimeo", "25", "75"], ["Telegram", "32.31", "67.69"], ["Dailymotion.com", "33.33", "66.67"], ["Twitter", "39.18", "60.82"], ["Facebook", "41.86", "58.14"], ["Tumblr", "50", "50"], ["Websites (including comments)", "51.95", "48.05"], ["VK.com", "55.41", "44.59"], ["Other Social Media Sites", "55.56", "44.44"], ["Blogs", "60.98", "39.02"], ["Google+", "91.67", "8.33"]], "labels": {"name": "Platform", "values": ["Instagram", "Forums", "YouTube", "Vimeo", "Telegram", "Dailymotion.com", "Twitter", "Facebook", "Tumblr", "Websites (including comments)", "VK.com", "Other Social Media Sites", "Blogs", "Google+"]}, "metadata": {"link": "http://www.inach.net/the-state-of-cyber-hate/ ", "type": "Solution", "unit": "Hate speech content removed (%)", "year": "2018", "title": "Distribution of Hate Speech Content Removal Across the Online Platforms (2018)", "topic": "Hate Speech", "method": "Data collection", "source": "Berecz, Tamás and Charlotte Devinat. The State of Cyber Hate (Brussels: International Network Against Cyber Hate, 2018)", "sub_topic": "Removal of hate speech", "chart_number": "5", "geographical": "European Union"}, "description": "The chart presents the distribution of hate speech content removal by the online platforms based on data collected by International Network Against Cyber Hate. The 2018 report found that Instagram and Forums (as a whole) were most likely to remove the flagged content, while Google+ was by far least likely to do so."},
{"data": [{"data": [45, 59, 72, 67, 39, 40, 54, 80, 50, 65, 70, 72], "name": "Percent of cases removed (approximate)"}, {"data": [null, null, null, 61, 59, 55, 50, 53, 56, 62, 66, 64], "name": "Moving average (Interval 4)"}], "_data": [["Month 2017", "Percent of cases removed (approximate)", "Moving average (Interval 4)"], ["January", "45", " "], ["February", "59", " "], ["March", "72", " "], ["April", "67", "61"], ["May", "39", "59"], ["June", "40", "55"], ["July", "54", "50"], ["August", "80", "53"], ["September", "50", "56"], ["October", "65", "62"], ["November", "70", "66"], ["December", "72", "64"]], "labels": {"name": "Month 2017", "values": ["January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"]}, "metadata": {"link": "http://www.inach.net/the-state-of-cyber-hate/ ", "type": "Solution", "unit": "Per cent (%)", "year": "2017", "title": "Trend in Removal Rates on Facebook Based on the Moving Averages of Percentage of Removed Cases (2017)", "topic": "Hate Speech", "method": "Data collection", "source": "Berecz, Tamás and Charlotte Devinat. The State of Cyber Hate (Brussels: International Network Against Cyber Hate, 2018)", "sub_topic": "Removal of hate speech", "chart_number": "6", "geographical": "European Union"}, "description": "The chart presents the share of the reported content which was removed by Facebook, based on data collected by the International Network Against Cyber Hate. The report found that, in 2017, Facebook's monthly removal rate varied widely, reaching a maximum level in August (80%) and a minimum in May (around 40%). Overall, Facebook's removal rate trended slightly upward in 2017."},
{"data": [{"data": [72, 90, 40, 70, 27, 57, 80, 80, 29, 42, 9, 18], "name": "Percent of cases removed (approximate)"}, {"data": [null, null, null, 68, 57, 49, 59, 61, 62, 58, 40, 25], "name": "Moving average (Interval 4)"}], "_data": [["Month (2017)", "Percent of cases removed (approximate)", "Moving average (Interval 4)"], ["January", "72", " "], ["February", "90", " "], ["March", "40", " "], ["April", "70", "68"], ["May", "27", "57"], ["June", "57", "49"], ["July", "80", "59"], ["August", "80", "61"], ["September", "29", "62"], ["October", "42", "58"], ["November", "9", "40"], ["December", "18", "25"]], "labels": {"name": "Month (2017)", "values": ["January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"]}, "metadata": {"link": "http://www.inach.net/the-state-of-cyber-hate/ ", "type": "Solution", "unit": "Per cent (%)", "year": "2017", "title": "Trend in Removal Rates on Twitter Based on the Moving Averages of Percentage of Removed Cases (2017)", "topic": "Hate Speech", "method": "Data collection", "source": "Berecz, Tamás and Charlotte Devinat. The State of Cyber Hate (Brussels: International Network Against Cyber Hate, 2018)", "sub_topic": "Removal of hate speech", "chart_number": "7", "geographical": "European Union"}, "description": "The chart presents the share of the reported content which was removed by Twitter, based on data collected by the International Network Against Cyber Hate. The report found that, in 2017, Twitter's monthly removal rate has a high variation, and recorded a maximum level in February (90%) and a minimum in November (around 10%). Overall, Twitter’s removal rate shows a steep downward trend in 2017."},
{"data": [{"data": [90, 75, 86, 90, 59, 22, 88, 72, 48, 63, 87, 68], "name": "Percent of cases removed (approximate)"}, {"data": [null, null, null, 85.3, 77.5, 64.3, 64.8, 60.3, 57.5, 67.8, 67.5, 66.5], "name": "Moving average (Interval 4)"}], "_data": [["Month ", "Percent of cases removed (approximate)", "Moving average (Interval 4)"], ["January", "90", " "], ["February", "75", " "], ["March", "86", " "], ["April", "90", "85.3"], ["May", "59", "77.5"], ["June", "22", "64.3"], ["July", "88", "64.8"], ["August", "72", "60.3"], ["September", "48", "57.5"], ["October", "63", "67.8"], ["November", "87", "67.5"], ["December", "68", "66.5"]], "labels": {"name": "Month ", "values": ["January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"]}, "metadata": {"link": "http://www.inach.net/the-state-of-cyber-hate/ ", "type": "Solution", "unit": "Per cent (%)", "year": "2017", "title": "Trend in Removal Rates on Youtube Based on the Moving Averages of Percentage of Removed Cases (2017)", "topic": "Hate Speech", "method": "Data collection", "source": "Berecz, Tamás and Charlotte Devinat. The State of Cyber Hate (Brussels: International Network Against Cyber Hate, 2018)", "sub_topic": "Removal of hate speech", "chart_number": "8", "geographical": "European Union"}, "description": "The chart presents the share of the reported content which was removed by YouTube, based on data collected by the International Network Against Cyber Hate. The report found that, in 2017, YouTube’s monthly removal rate was highly volatile, recording maximum levels in January and April (90%) and minimum one in June (around 22%). Overall, YouTube’s removal rate has a slight downward trend in 2017."},
{"data": [{"data": [50, 68, 89, 92, 95.7, 81.5], "name": "Facebook"}, {"data": [23, 39, 80, 88, 76.6, 81.8], "name": "Twitter"}, {"data": [60, 42, 61, 80, 81.5, 88.8], "name": "YouTube"}, {"data": [null, null, null, 78, 91.8, 62.4], "name": "Instagram"}, {"data": [null, null, null, 60, null, null], "name": "Google+"}, {"data": [null, null, null, null, 100, 100], "name": "Jeuxvideo.com"}, {"data": [null, null, null, null, null, 82.5], "name": "TikTok"}, {"data": [40, 50, 80, 89, 90.4, 81], "name": "Average of companies"}], "_data": [["Share of notification reviewed", "Facebook", "Twitter", "YouTube", "Instagram", "Google+", "Jeuxvideo.com", "TikTok", "Average of companies"], ["Dec 2016 <br />1st monitoring", "50", "23", "60", "", "", "", "", "40"], ["May 2017 <br />2nd monitoring", "68", "39", "42", "", "", "", "", "50"], ["Dec 2017  <br />3rd monitoring", "89", "80", "61", "", "", "", "", "80"], ["Dec 2018 <br/>4th monitoring", "92", "88", "80", "78", "60", "", "", "89"], ["Dec 2019 <br/>5th monitoring", "95.7", "76.6", "81.5", "91.8", "", "100", "", "90.4"], ["2021 <br/>6th monitoring", "81.5", "81.8", "88.8", "62.4", "", "100", "82.5", "81"]], "labels": {"name": "Share of notification reviewed", "values": ["Dec 2016 <br />1st monitoring", "May 2017 <br />2nd monitoring", "Dec 2017  <br />3rd monitoring", "Dec 2018 <br/>4th monitoring", "Dec 2019 <br/>5th monitoring", "2021 <br/>6th monitoring"]}, "metadata": {"link": "https://ec.europa.eu/info/sites/default/files/factsheet-6th-monitoring-round-of-the-code-of-conduct_october2021_en_1.pdf", "type": "Solution", "unit": "Share of notification reviewed (%)", "year": "2016-2021", "Range": "0 100", "title": "Trend of the Rate of Notifications Assessed Within 24 Hours", "topic": "Hate Speech", "method": "Self-reporting", "source": "European Commission. Sixth Evaluation on the Code of Conduct on Countering Illegal Hate Speech Online (Brussels: European Commission, 2021)  ", "sub_topic": "Removal of hate speech", "chart_number": "9", "geographical": "European Union"}, "description": "The chart shows the trend of the share of notifications reviewed within 24 hours by the tech companies over the period December 2016 - Octomber 2021, based on the data reported by social media platforms participating in the European Commission's Code of conduct. In 2021, Jeuxvideo.com assessed all notifications in less than 24h, while YouTube did so for 88.8% and Facebook for 81.5%. Among the platforms participating in the Code of conduct, Instagram has the lowest rate of review (62.4%). On average, the rate of review of companies remain high (81%), but shows a slight decline compared to 2020 (90.4%)."},
{"data": [{"data": [28.3, 19.1, 48.5, null, null], "name": "First monitoring (December 2016)"}, {"data": [66.5, 37.4, 66, null, null], "name": "Second monitoring (May 2017)"}, {"data": [79.8, 45.7, 75, null, null], "name": "Thrid monitoring (December 2017)"}, {"data": [82.4, 43.5, 85.4, 70.6, 80], "name": "Fourth monitoring (December 2018)"}], "_data": [["Channel", "First monitoring (December 2016)", "Second monitoring (May 2017)", "Thrid monitoring (December 2017)", "Fourth monitoring (December 2018)"], ["Facebook", "28.3", "66.5", "79.8", "82.4"], ["Twitter", "19.1", "37.4", "45.7", "43.5"], ["YouTube", "48.5", "66", "75", "85.4"], ["Instagram", "", "", "", "70.6"], ["Google+", "", "", "", "80"]], "labels": {"name": "Channel", "values": ["Facebook", "Twitter", "YouTube", "Instagram", "Google+"]}, "metadata": {"link": "https://ec.europa.eu/info/sites/info/files/code_of_conduct_factsheet_7_web.pdf", "type": "Solution", "unit": "Share of content removed", "year": "2018", "title": "Rate of Hate Speech Content Removal Across ICT Companies (2018)", "topic": "Hate Speech", "method": "Self-reporting", "source": "European Commission. Fourth Evaluation on the Code of Conduct on Countering Illegal Hate Speech Online (Brussels: European Commission, 2019)  ", "sub_topic": "Removal of hate speech", "chart_number": "10", "geographical": "European Union"}, "description": "The chart presents the distribution of hate speech content removal by the ICT companies, based on data reported by social media platforms participating in the European Commission's Code of conduct. The data shows that out of the platforms participating in the Code of conduct, YouTube now has the highest rate of removal, while Twitter has the lowest. Facebook and YouTube have increased their rates of removal significantly, while Twitter's increase has been less dramatic."},
{"data": [{"data": [17, 15.6, 13, 12.2, 10.1, 6.2, 5.3, 5.2, 5.1, 4.9, 3.7, 1.7], "name": "Percent (%)"}], "_data": [["Type", "Percent (%)"], ["Xenophobia (including anti-migrant hatred)", "17"], ["Sexual orientation", "15.6"], ["Anti-Muslim hatred", "13"], ["Anti-gypsyism", "12.2"], ["Antisemitism", "10.1"], ["Ethnic origin", "6.2"], ["Race", "5.3"], ["Religion", "5.2"], ["National origin", "5.1"], ["Afrophobia", "4.9"], ["Gender identity", "3.7"], ["Other", "1.7"]], "labels": {"name": "Type", "values": ["Xenophobia (including anti-migrant hatred)", "Sexual orientation", "Anti-Muslim hatred", "Anti-gypsyism", "Antisemitism", "Ethnic origin", "Race", "Religion", "National origin", "Afrophobia", "Gender identity", "Other"]}, "metadata": {"link": "https://ec.europa.eu/info/sites/info/files/code_of_conduct_factsheet_7_web.pdf", "type": "Solution", "unit": "Per cent (%)", "year": "2019", "title": "Grounds of Hatred Reported by Social Media Platforms", "topic": "Hate Speech", "method": "Self-reporting", "source": "European Commission. Fourth Evaluation on the Code of Conduct on Countering Illegal Hate Speech Online (Brussels: European Commission, 2019)  ", "sub_topic": "Types of hate speech", "chart_number": "12", "geographical": "European Union"}, "description": "The chart shows the grounds of hatred reported for reviewed posts, based on data reported by social media platforms participating in the European Commission's Code of conduct. Xenophobia and sexual orientation were the most common grounds for hatred, while gender identity and afrophobia were the least common grounds for hatred."},
{"data": [{"data": [55, 51, 46, 47, 46, 42, 41], "name": "Men"}, {"data": [47, 46, 43, 43, 40, 32, 35], "name": "Women"}], "_data": [["Type of hate speech", "Men", "Women"], ["Racist statements", "55", "47"], ["Islamophobic statements", "51", "46"], ["Homophobic statements", "46", "43"], ["Xenophobic statements", "47", "43"], ["Antisemitic comments", "46", "40"], ["Conspiracy theories", "42", "32"], ["Negationist statements", "41", "35"]], "labels": {"name": "Type of hate speech", "values": ["Racist statements", "Islamophobic statements", "Homophobic statements", "Xenophobic statements", "Antisemitic comments", "Conspiracy theories", "Negationist statements"]}, "metadata": {"link": "https://www.opinion-way.com/fr/sondage-d-opinion/sondages-publies/uejf-les-propos-haineux-sur-internet-par-opinionway-fevrier-2015/viewdocument/1103.html", "type": "Problem", "unit": "Percent (%)", "year": "2015", "title": "Share of People Who Faced Hate Speech Online in France (2015)", "topic": "Hate Speech", "method": "Survey (N =1006)", "source": "OpinionWay, Union des Étudiants Juifs en France. Les Français et les propos haineux sur Internet, published on OpinionWay, 23 Février 2015.", "sub_topic": "Prevalence of hate speech", "chart_number": "15", "geographical": "France"}, "description": "The chart shows the exposure to hateful online content on the internet, based on the results of a survey conducted in France in February 2015. Results show that men were generally more exposed to this type of content compared to women. 55% of male respondents came across racist statements online, while only 47% of women respondents did so."},
{"data": [{"data": [15, 12, 10, 5, 2, 2], "name": "Totally trust"}, {"data": [55, 54, 53, 42, 25, 24], "name": "Tend to trust"}, {"data": [14, 21, 19, 23, 32, 36], "name": "Tend not to trust"}, {"data": [6, 8, 8, 10, 16, 18], "name": "Do not trust at all"}, {"data": [10, 5, 10, 20, 25, 20], "name": "Don't know"}], "_data": [["Horizontal: Degree of trust in news source. Vertical: news source.", "Totally trust", "Tend to trust", "Tend not to trust", "Do not trust at all", "Don't know"], ["Radio", "15", "55", "14", "6", "10"], ["Television", "12", "54", "21", "8", "5"], ["Printed newspapers and magazines", "10", "53", "19", "8", "10"], ["Online newspapers and news magazines", "5", "42", "23", "10", "20"], ["Video hosting websites and podcasts", "2", "25", "32", "16", "25"], ["Online social networkers and messaging apps", "2", "24", "36", "18", "20"]], "labels": {"name": "Horizontal: Degree of trust in news source. Vertical: news source.", "values": ["Radio", "Television", "Printed newspapers and magazines", "Online newspapers and news magazines", "Video hosting websites and podcasts", "Online social networkers and messaging apps"]}, "metadata": {"link": "https://publications.europa.eu/en/publication-detail/-/publication/2d79b85a-4cea-11e8-be1d-01aa75ed71a1/language-en", "type": "Problem", "unit": "Per cent (%)", "year": "2018", "title": "Levels of Trust in News and Information from Different Sources at European Union Level", "topic": "Disinformation", "method": "Survey (N = 26576)", "source": "European Commission. Flash Eurobarometer 464 Report: Fake News and Disinformation Online (Brussels:European Commission, 2018)", "sub_topic": "Trust in sources of news", "chart_number": "16", "geographical": "European Union"}, "description": "The Eurobarometer survey’s data shows that respondents were more likely to trust traditional news sources, such as radio, television, and printed newspapers and news magazines, than they were to trust online sources. Also, the respondents were more likely to report that they didn't know how much they trusted online sources. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [3, 2, 5, 3, 5, 3, 2, 4, 3, 1, 3, 1, 1, 3, 3, 3, 2, 2, 2, 3, 2, 1, 3, 2, 1, 1, 1, 2, 1], "name": "Totally trust"}, {"data": [38, 38, 34, 36, 33, 35, 35, 32, 32, 33, 31, 32, 32, 29, 28, 28, 29, 27, 27, 25, 25, 26, 24, 24, 24, 24, 18, 15, 16], "name": "Tend to trust"}, {"data": [25, 29, 20, 34, 28, 39, 26, 31, 35, 33, 36, 24, 36, 26, 28, 29, 36, 37, 44, 27, 36, 32, 42, 36, 34, 40, 44, 40, 47], "name": "Tend not to trust"}, {"data": [15, 7, 13, 6, 15, 8, 3, 10, 20, 10, 10, 5, 9, 23, 9, 15, 16, 12, 19, 11, 10, 25, 23, 18, 18, 13, 23, 20, 19], "name": "Do not trust at all"}, {"data": [19, 24, 28, 21, 19, 15, 34, 23, 10, 23, 20, 38, 22, 19, 32, 25, 17, 22, 8, 34, 27, 16, 8, 20, 23, 22, 14, 23, 17], "name": "Don't know"}], "_data": [["Country", "Totally trust", "Tend to trust", "Tend not to trust", "Do not trust at all", "Don't know"], ["Portugal", "3", "38", "25", "15", "19"], ["Croatia", "2", "38", "29", "7", "24"], ["Romania", "5", "34", "20", "13", "28"], ["Latvia", "3", "36", "34", "6", "21"], ["Cyprus", "5", "33", "28", "15", "19"], ["Netherlands", "3", "35", "39", "8", "15"], ["Estonia", "2", "35", "26", "3", "34"], ["Slovakia", "4", "32", "31", "10", "23"], ["Belgium", "3", "32", "35", "20", "10"], ["Poland", "1", "33", "33", "10", "23"], ["Denmark", "3", "31", "36", "10", "20"], ["Letonia", "1", "32", "24", "5", "38"], ["Finland", "1", "32", "36", "9", "22"], ["Greece", "3", "29", "26", "23", "19"], ["Malta", "3", "28", "28", "9", "32"], ["Bulgaria", "3", "28", "29", "15", "25"], ["Ireland", "2", "29", "36", "16", "17"], ["Slovenia", "2", "27", "37", "12", "22"], ["Luxembourg", "2", "27", "44", "19", "8"], ["Hungary", "3", "25", "27", "11", "34"], ["Czech Republic", "2", "25", "36", "10", "27"], ["France", "1", "26", "32", "25", "16"], ["Spain", "3", "24", "42", "23", "8"], ["European Union", "2", "24", "36", "18", "20"], ["United Kingdom", "1", "24", "34", "18", "23"], ["Sweden", "1", "24", "40", "13", "22"], ["Italy", "1", "18", "44", "23", "14"], ["Germany", "2", "15", "40", "20", "23"], ["Austria", "1", "16", "47", "19", "17"]], "labels": {"name": "Country", "values": ["Portugal", "Croatia", "Romania", "Latvia", "Cyprus", "Netherlands", "Estonia", "Slovakia", "Belgium", "Poland", "Denmark", "Letonia", "Finland", "Greece", "Malta", "Bulgaria", "Ireland", "Slovenia", "Luxembourg", "Hungary", "Czech Republic", "France", "Spain", "European Union", "United Kingdom", "Sweden", "Italy", "Germany", "Austria"]}, "metadata": {"link": "https://publications.europa.eu/en/publication-detail/-/publication/2d79b85a-4cea-11e8-be1d-01aa75ed71a1/language-en", "type": "Problem", "unit": "Share of respondents (%)", "year": "2018", "title": "Levels of Trust in News and Information From Online Social Networks and Messaging Apps Across European Union Member States", "topic": "Disinformation", "method": "Survey (N = 26576)", "source": "European Commission. Flash Eurobarometer 464 Report: Fake News and Disinformation Online (Brussels:European Commission, 2018)", "sub_topic": "Trust in sources of news", "chart_number": "17", "geographical": "European Union"}, "description": "The chart shows the distribution of the responses to the question, \"How much do you trust or not the news and information you access through online social networks and messaging apps?\" of participants in the Eurobarometer survey. The data shows that respondents from Portugal were the most likely to trust this type of information, while respondents from Austria were the least likely to do so. Among all Europeans, 26% of respondents said that they trusted this information. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [37, 31, 12, 17, 3], "name": "Per cent"}], "_data": [["Frequency", "Per cent"], ["Every day or almost everyday", "37"], ["At least once a week", "31"], ["Several times a month", "12"], ["Seldom or never", "17"], ["Don't know", "3"]], "labels": {"name": "Frequency", "values": ["Every day or almost everyday", "At least once a week", "Several times a month", "Seldom or never", "Don't know"]}, "metadata": {"link": "https://publications.europa.eu/en/publication-detail/-/publication/2d79b85a-4cea-11e8-be1d-01aa75ed71a1/language-en", "type": "Problem", "unit": "Per cent (%)", "year": "2018", "title": "Frequency of Encountering Disinformation at the European Union Level", "topic": "Disinformation", "method": "Survey (N=26576). ", "source": "European Commission. Flash Eurobarometer 464 Report: Fake News and Disinformation Online (Brussels:European Commission, 2018)", "sub_topic": "Prevalence of disinformation", "chart_number": "18", "geographical": "European Union"}, "description": "The results of the Eurobarometer survey show that more than 60% of respondents reported encountering information or news that they believed misrepresented reality or was even false at least once per week. Only 17% reported that they did so seldom or never. The respondents were asked \"Q.2 How often do you come across news or information that you believe misrepresent reality or is even false?\" European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [53, 52, 47, 48, 39, 55, 45, 38, 38, 33, 41, 40, 37, 37, 40, 31, 35, 31, 31, 45, 32, 25, 25, 25, 24, 27, 23, 23, 18], "name": "Everyday or almost every day"}, {"data": [25, 25, 29, 27, 36, 19, 28, 33, 31, 36, 27, 28, 31, 31, 27, 35, 28, 32, 32, 16, 29, 34, 33, 33, 33, 27, 31, 30, 32], "name": "At least once a week"}, {"data": [9, 7, 11, 7, 9, 18, 10, 11, 15, 13, 9, 13, 12, 11, 8, 14, 12, 15, 12, 20, 14, 18, 15, 15, 14, 13, 14, 16, 18], "name": "Several times a month"}, {"data": [11, 12, 9, 14, 14, 6, 15, 17, 12, 15, 16, 14, 17, 18, 20, 18, 20, 18, 21, 15, 21, 19, 20, 22, 25, 21, 23, 21, 29], "name": "Seldom or never"}, {"data": [2, 4, 4, 4, 2, 2, 2, 1, 4, 3, 7, 5, 3, 3, 5, 2, 5, 4, 4, 4, 4, 4, 7, 5, 4, 12, 9, 10, 3], "name": "Don't know"}], "_data": [["Horizontal: Frequency. Vertical: EU country.", "Everyday or almost every day", "At least once a week", "Several times a month", "Seldom or never", "Don't know"], ["Spain", "53", "25", "9", "11", "2"], ["Hungary", "52", "25", "7", "12", "4"], ["Croatia", "47", "29", "11", "9", "4"], ["Poland", "48", "27", "7", "14", "4"], ["France", "39", "36", "9", "14", "2"], ["Greece", "55", "19", "18", "6", "2"], ["Slovakia", "45", "28", "10", "15", "2"], ["Luxembourg", "38", "33", "11", "17", "1"], ["United Kingdom", "38", "31", "15", "12", "4"], ["Ireland", "33", "36", "13", "15", "3"], ["Romania", "41", "27", "9", "16", "7"], ["Czech Republic", "40", "28", "13", "14", "5"], ["European Union", "37", "31", "12", "17", "3"], ["Italy", "37", "31", "11", "18", "3"], ["Bulgaria", "40", "27", "8", "20", "5"], ["Austria", "31", "35", "14", "18", "2"], ["Latvia", "35", "28", "12", "20", "5"], ["Belgium", "31", "32", "15", "18", "4"], ["Slovenia", "31", "32", "12", "21", "4"], ["Cyprus", "45", "16", "20", "15", "4"], ["Portugal", "32", "29", "14", "21", "4"], ["Netherlands", "25", "34", "18", "19", "4"], ["Denmark", "25", "33", "15", "20", "7"], ["Sweden", "25", "33", "15", "22", "5"], ["Germany", "24", "33", "14", "25", "4"], ["Malta", "27", "27", "13", "21", "12"], ["Estonia", "23", "31", "14", "23", "9"], ["Latvia", "23", "30", "16", "21", "10"], ["Finland", "18", "32", "18", "29", "3"]], "labels": {"name": "Horizontal: Frequency. Vertical: EU country.", "values": ["Spain", "Hungary", "Croatia", "Poland", "France", "Greece", "Slovakia", "Luxembourg", "United Kingdom", "Ireland", "Romania", "Czech Republic", "European Union", "Italy", "Bulgaria", "Austria", "Latvia", "Belgium", "Slovenia", "Cyprus", "Portugal", "Netherlands", "Denmark", "Sweden", "Germany", "Malta", "Estonia", "Latvia", "Finland"]}, "metadata": {"link": "https://publications.europa.eu/en/publication-detail/-/publication/2d79b85a-4cea-11e8-be1d-01aa75ed71a1/language-en", "type": "Problem", "unit": "Per cent (%)", "year": "2018", "title": "Frequency of Encountering Disinformation Online (by Country)", "topic": "Disinformation", "method": "Survey (N=26576) ", "source": "European Commission. Flash Eurobarometer 464 Report: Fake News and Disinformation Online (Brussels:European Commission, 2018)", "sub_topic": "Prevalence of disinformation", "chart_number": "19", "geographical": "European Union"}, "description": "This chart shows the data collected via survey for the Flash Eurobarometer 464. The data shows the frequency with which respondents reported encountering information that they believe misrepresents reality or is even false. Respondents from Spain reported encountering such information with the highest frequency, while respondents from Finland reported encountering such information least frequently. The respondents were asked \"Q2 How often do you come across news or information that misrepresent reality or even false a problem in your country?\" European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [69, 63, 60, 61, 69, 43, 51, 44, 61, 62, 43, 44, 40, 38, 32, 49, 39, 42, 46, 36, 39, 43, 26, 24, 29, 27, 17, 21, 28], "name": "Yes, definitely"}, {"data": [22, 27, 30, 28, 20, 45, 37, 43, 25, 24, 43, 41, 45, 47, 52, 35, 45, 42, 37, 47, 43, 37, 52, 53, 46, 46, 56, 52, 42], "name": "Yes, to some extent"}, {"data": [3, 3, 6, 5, 6, 5, 5, 8, 9, 7, 8, 9, 8, 10, 9, 9, 12, 12, 10, 11, 11, 13, 17, 17, 18, 18, 19, 21, 22], "name": "No, not really"}, {"data": [2, 5, 2, 2, 3, 4, 5, 3, 1, 3, 3, 3, 5, 3, 3, 3, 2, 2, 3, 3, 3, 4, 3, 4, 4, 3, 4, 4, 5], "name": "No, definitely not"}, {"data": [4, 2, 2, 4, 2, 3, 2, 2, 4, 4, 3, 3, 2, 2, 4, 4, 2, 2, 4, 3, 4, 3, 2, 2, 3, 6, 4, 2, 3], "name": "Don't know"}], "_data": [["Horizontal: Agreement. Vertical: EU country.", "Yes, definitely", "Yes, to some extent", "No, not really", "No, definitely not", "Don't know"], ["Cyprus", "69", "22", "3", "2", "4"], ["Greece", "63", "27", "3", "5", "2"], ["Italy", "60", "30", "6", "2", "2"], ["Hungary", "61", "28", "5", "2", "4"], ["Bulgaria", "69", "20", "6", "3", "2"], ["Slovenia", "43", "45", "5", "4", "3"], ["Spain", "51", "37", "5", "5", "2"], ["Slovakia", "44", "43", "8", "3", "2"], ["Malta", "61", "25", "9", "1", "4"], ["Romania", "62", "24", "7", "3", "4"], ["Croatia", "43", "43", "8", "3", "3"], ["European Union", "44", "41", "9", "3", "3"], ["France", "40", "45", "8", "5", "2"], ["Sweden", "38", "47", "10", "3", "2"], ["United Kindgdom", "32", "52", "9", "3", "4"], ["Poland", "49", "35", "9", "3", "4"], ["Germany", "39", "45", "12", "2", "2"], ["Portugal", "42", "42", "12", "2", "2"], ["Letonia", "46", "37", "10", "3", "4"], ["Austria", "36", "47", "11", "3", "3"], ["Latvia", "39", "43", "11", "3", "4"], ["Czech Republic", "43", "37", "13", "4", "3"], ["Netherlands", "26", "52", "17", "3", "2"], ["Ireland", "24", "53", "17", "4", "2"], ["Finland", "29", "46", "18", "4", "3"], ["Estonia", "27", "46", "18", "3", "6"], ["Denmark", "17", "56", "19", "4", "4"], ["Luxembourg", "21", "52", "21", "4", "2"], ["Belgium", "28", "42", "22", "5", "3"]], "labels": {"name": "Horizontal: Agreement. Vertical: EU country.", "values": ["Cyprus", "Greece", "Italy", "Hungary", "Bulgaria", "Slovenia", "Spain", "Slovakia", "Malta", "Romania", "Croatia", "European Union", "France", "Sweden", "United Kindgdom", "Poland", "Germany", "Portugal", "Letonia", "Austria", "Latvia", "Czech Republic", "Netherlands", "Ireland", "Finland", "Estonia", "Denmark", "Luxembourg", "Belgium"]}, "metadata": {"link": "https://publications.europa.eu/en/publication-detail/-/publication/2d79b85a-4cea-11e8-be1d-01aa75ed71a1/language-en", "type": "Problem", "unit": "Per cent (%)", "year": "2018", "title": "Europeans' Views on Whether Disinformation is a Problem in Their Country", "topic": "Disinformation", "method": "Survey (N=26576)", "source": "European Commission. Flash Eurobarometer 464 Report: Fake News and Disinformation Online (Brussels:European Commission, 2018)", "sub_topic": "Prevalence of disinformation", "chart_number": "20", "geographical": "European Union"}, "description": "According to the results of the Eurobarometer survey, in all countries, more than half of respondents viewed the existence of news or information that misrepresents reality or is even false as a problem. Over 90% of respondents from Cyprus, Greece and Italy view this kind of information as problem in their country, while in Belgium only 66% share this view. The respondents were asked \"Q4.1 In your opinion, is the existence of news or information that misrepresent reality or even false a problem in your country?\" European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [41.5, 36.5, 31.1, 27.5, 26.8, 26.5, 23.9, 22.7, 22.2, 21.9, 20.5, 20.5, 17.2, 16.3, 15.3, 13.5, 13.2, 12.4, 11.3, 11.3, 10.4, 10.1, 9.5, 9.5, 9.3, 8.6, 4.1, 2.4], "name": "Respondents"}], "_data": [["Object", "Respondents"], ["Government and central institutions", "41.5"], ["Opposition parties", "36.5"], ["Education sytem and teachers", "31.1"], ["Governmental coalition parties", "27.5"], ["Actors, musicians, celebrities, etc", "26.8"], ["Individual politicians", "26.5"], ["Catholic Church", "23.9"], ["Health care and doctors", "22.7"], ["Image of Poland", "22.2"], ["Judicial system", "21.9"], ["Sexual minorities", "20.5"], ["Image of Poles", "20.5"], ["Private individuals", "17.2"], ["Media and journalists", "16.3"], ["Image of foreigners", "15.3"], ["Products and/or services", "13.5"], ["Social activists", "13.2"], ["Image of other countries", "12.4"], ["Local government", "11.3"], ["Banks", "11.3"], ["Police", "10.4"], ["NGOs", "10.1"], ["Trade unions", "9.5"], ["Religious minorities", "9.5"], ["Companies and/or corporations", "9.3"], ["Ethnic/national minorities", "8.6"], ["Army", "4.1"], ["Others", "2.4"]], "labels": {"name": "Object", "values": ["Government and central institutions", "Opposition parties", "Education sytem and teachers", "Governmental coalition parties", "Actors, musicians, celebrities, etc", "Individual politicians", "Catholic Church", "Health care and doctors", "Image of Poland", "Judicial system", "Sexual minorities", "Image of Poles", "Private individuals", "Media and journalists", "Image of foreigners", "Products and/or services", "Social activists", "Image of other countries", "Local government", "Banks", "Police", "NGOs", "Trade unions", "Religious minorities", "Companies and/or corporations", "Ethnic/national minorities", "Army", "Others"]}, "metadata": {"link": "https://kometa.edu.pl/biblioteka-cyfrowa/publikacja,744,bezpieczne-wybory-badanie-opinii-o-dezinformacji-w-sieci", "type": "Problem", "unit": "Share of respondents (%)", "year": "2019", "title": "Objects Most Often Attacked by Disinformation or Manipulation on the Internet in Poland (2019, in Polish)", "topic": "Disinformation", "method": "Survey", "source": "Bochenek, Marcin and Rafał Lange. Bezpieczne Wybory: Badanie Opinii o (Dez)informacji w Sieci (in Polish) (Warsaw: NASK Państwowy Instytut Badawczy, 2019)", "sub_topic": "Targets of disinformation", "chart_number": "22", "geographical": "Poland"}, "description": "The chart illustrated the results of a 2019 survey that looked at the perception of Polish internet users towards the most often objects targeted by disinformation or Internet manipulation. The results showed that Poles identified the government and central institutions and opposition parties as the subjects most often targeted by this type of attacks. On the other hand, the least targeted objects were the ethnic or national minorities or the army."},
{"data": [{"data": [22.3, 19, 14.7, 13.8, 12.6, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], "name": "Prominent"}, {"data": [null, null, null, null, null, 1.7, 1.4, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], "name": "Russian News sites"}, {"data": [null, null, null, null, null, null, null, 3.1, 1.1, 1, 1, 0.9, 0.5, 0.4, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.2, 0.2, 0.2, 0.2, 0.2], "name": "False news site"}], "_data": [["Site", "Prominent", "Russian News sites", "False news site"], ["www.lefigaro.fr", "22.3", "0", "0"], ["www.lemonde.fr", "19", "0", "0"], ["www.francetvinfo.fr", "14.7", "0", "0"], ["www.huffingtonpost.fr", "13.8", "0", "0"], ["www.20mitues.fr", "12.6", "0", "0"], ["francais.rt.com", "0", "1.7", "0"], ["fr.sputniknews.com", "0", "1.4", "0"], ["www.santeplusmag.com", "0", "0", "3.1"], ["www.santenatureinnovation.com", "0", "0", "1.1"], ["www.espritsciencemetaphysiques.com", "0", "0", "1"], ["eddenya.com", "0", "0", "1"], ["www.letopdelhumour.fr", "0", "0", "0.9"], ["www.egaliteetreconciliation.fr", "0", "0", "0.5"], ["lagauchematuer.fr", "0", "0", "0.4"], ["sante-nutrition.org", "0", "0", "0.3"], ["www.topsante.org", "0", "0", "0.3"], ["ripostelaique.com", "0", "0", "0.3"], ["www.dreuz.info", "0", "0", "0.3"], ["lesmoutonsenrages.fr", "0", "0", "0.3"], ["resistancerepulicaine.eu", "0", "0", "0.3"], ["www.nouvelordremondial.cc", "0", "0", "0.3"], ["reseauinternational.net", "0", "0", "0.3"], ["lesobervateurs.ch", "0", "0", "0.2"], ["lesmoutonsrebelles.com", "0", "0", "0.2"], ["stopmensonges.com", "0", "0", "0.2"], ["www.breizh-info.com", "0", "0", "0.2"], ["breizatao.com", "0", "0", "0.2"]], "labels": {"name": "Site", "values": ["www.lefigaro.fr", "www.lemonde.fr", "www.francetvinfo.fr", "www.huffingtonpost.fr", "www.20mitues.fr", "francais.rt.com", "fr.sputniknews.com", "www.santeplusmag.com", "www.santenatureinnovation.com", "www.espritsciencemetaphysiques.com", "eddenya.com", "www.letopdelhumour.fr", "www.egaliteetreconciliation.fr", "lagauchematuer.fr", "sante-nutrition.org", "www.topsante.org", "ripostelaique.com", "www.dreuz.info", "lesmoutonsenrages.fr", "resistancerepulicaine.eu", "www.nouvelordremondial.cc", "reseauinternational.net", "lesobervateurs.ch", "lesmoutonsrebelles.com", "stopmensonges.com", "www.breizh-info.com", "breizatao.com"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/our-research/measuring-reach-fake-news-and-online-disinformation-europe", "type": "Problem", "unit": "Monthly reach (%)", "year": "2017", "title": "Average Monthly Reach of Prominent French News Sites and Some of The Most Popular False News Sites (2017)", "topic": "Disinformation", "method": "Data collection", "source": "Fletcher, Richard, Alessio Cornia, Lucas Graves and Rasmus Kleis. \"Measuring the Reach of “Fake News” and Online Disinformation in Europe,\" Reuters Institute, February 2018", "sub_topic": "Prevalence of disinformation", "chart_number": "23", "geographical": "France"}, "description": "The graph shows that all of the false news sites in the French sample have a comparatively small reach. On average, most reached just 1% or fewer of the French online population each month in 2017. The most popular, Santé+ Magazine—an outlet that has been shown by Les Décodeurs to publish demonstrably false health information—reached 3.1% (this equates to around 1.5 million people). This was more than double that of well-known Russian outlets like Russia Today (1.5%) and Sputnik News (1.4%), which despite their international prominence, are used only by a small minority."},
{"data": [{"data": [96.2, 172.8, 57.2, 47.3, 71.5, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], "name": "Prominent"}, {"data": [null, null, null, null, null, 7.7, 9, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], "name": "Russian news sites"}, {"data": [null, null, null, null, null, null, null, 9.4, 1.3, 2.5, 1.7, 3.4, 5.8, 0.7, 0.5, 0.5, 0.5, 0.9, 0.4, 0.6, 0.9, 0.2, 0.2, 0.3, 0.6, 0.3, 0.2], "name": "False news site"}], "_data": [["Site", "Prominent", "Russian news sites", "False news site"], ["www.lefigaro.fr", "96.2", "0", "0"], ["www.lemonde.fr", "172.8", "0", "0"], ["www.francetvinfo.fr", "57.2", "0", "0"], ["www.huffingtonpost.fr", "47.3", "0", "0"], ["www.20mitues.fr", "71.5", "0", "0"], ["francais.rt.com", "0", "7.7", "0"], ["fr.sputniknews.com", "0", "9", "0"], ["www.santeplusmag.com", "0", "0", "9.4"], ["www.santenatureinnovation.com", "0", "0", "1.3"], ["www.espritsciencemetaphysiques.com", "0", "0", "2.5"], ["eddenya.com", "0", "0", "1.7"], ["www.letopdelhumour.fr", "0", "0", "3.4"], ["www.egaliteetreconciliation.fr", "0", "0", "5.8"], ["lagauchematuer.fr", "0", "0", "0.7"], ["sante-nutrition.org", "0", "0", "0.5"], ["www.topsante.org", "0", "0", "0.5"], ["ripostelaique.com", "0", "0", "0.5"], ["www.dreuz.info", "0", "0", "0.9"], ["lesmoutonsenrages.fr", "0", "0", "0.4"], ["resistancerepulicaine.eu", "0", "0", "0.6"], ["www.nouvelordremondial.cc", "0", "0", "0.9"], ["reseauinternational.net", "0", "0", "0.2"], ["lesobervateurs.ch", "0", "0", "0.2"], ["lesmoutonsrebelles.com", "0", "0", "0.3"], ["stopmensonges.com", "0", "0", "0.6"], ["www.breizh-info.com", "0", "0", "0.3"], ["breizatao.com", "0", "0", "0.2"]], "labels": {"name": "Site", "values": ["www.lefigaro.fr", "www.lemonde.fr", "www.francetvinfo.fr", "www.huffingtonpost.fr", "www.20mitues.fr", "francais.rt.com", "fr.sputniknews.com", "www.santeplusmag.com", "www.santenatureinnovation.com", "www.espritsciencemetaphysiques.com", "eddenya.com", "www.letopdelhumour.fr", "www.egaliteetreconciliation.fr", "lagauchematuer.fr", "sante-nutrition.org", "www.topsante.org", "ripostelaique.com", "www.dreuz.info", "lesmoutonsenrages.fr", "resistancerepulicaine.eu", "www.nouvelordremondial.cc", "reseauinternational.net", "lesobervateurs.ch", "lesmoutonsrebelles.com", "stopmensonges.com", "www.breizh-info.com", "breizatao.com"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/our-research/measuring-reach-fake-news-and-online-disinformation-europe", "type": "Problem", "unit": "Milion minutes", "year": "2017", "title": "Average Monthly Time Spent With Prominent French News Sites And Some of The Most Popular False News Sites (2017)", "topic": "Disinformation", "method": "Data mining", "source": "Fletcher, Richard, Alessio Cornia, Lucas Graves and Rasmus Kleis. \"Measuring the Reach of “Fake News” and Online Disinformation in Europe,\" Reuters Institute, February 2018", "sub_topic": "Prevalence of disinformation", "chart_number": "24", "geographical": "France"}, "description": "Cumulative data of total time spent with the false news outlet (per month) remains below the time spent with news. Even if people spent just under 50 million minutes per month with Le Huffington Post, this still exceeds the total time spent with all 20 false news sites from the sample."},
{"data": [{"data": [23.1, 20.6, 13.5, 13.4, 56.6, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], "name": "Prominent"}, {"data": [null, null, null, null, null, 5.1, 4.2, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], "name": "Russian news sites"}, {"data": [null, null, null, null, null, null, null, 113.7, 0.1, 1.6, 1, 58.7, null, 14.8, null, null, 0.5, 0.6, 0.1, null, 0.2, 1.1, null, null, 0.5, 0.2, 0.1], "name": "False news site"}], "_data": [["Site", "Prominent", "Russian news sites", "False news site"], ["www.lefigaro.fr", "23.1", "0", "0"], ["www.lemonde.fr", "20.6", "0", "0"], ["www.francetvinfo.fr", "13.5", "0", "0"], ["www.huffingtonpost.fr", "13.4", "0", "0"], ["www.20mitues.fr", "56.6", "0", "0"], ["francais.rt.com", "0", "5.1", "0"], ["fr.sputniknews.com", "0", "4.2", "0"], ["www.santeplusmag.com", "0", "0", "113.7"], ["www.santenatureinnovation.com", "0", "0", "0.1"], ["www.espritsciencemetaphysiques.com", "0", "0", "1.6"], ["eddenya.com", "0", "0", "1"], ["www.letopdelhumour.fr", "0", "0", "58.7"], ["www.egaliteetreconciliation.fr", "0", "0", "0"], ["lagauchematuer.fr", "0", "0", "14.8"], ["sante-nutrition.org", "0", "0", "0"], ["www.topsante.org", "0", "0", "0"], ["ripostelaique.com", "0", "0", "0.5"], ["www.dreuz.info", "0", "0", "0.6"], ["lesmoutonsenrages.fr", "0", "0", "0.1"], ["resistancerepulicaine.eu", "0", "0", "0"], ["www.nouvelordremondial.cc", "0", "0", "0.2"], ["reseauinternational.net", "0", "0", "1.1"], ["lesobervateurs.ch", "0", "0", "0"], ["lesmoutonsrebelles.com", "0", "0", "0"], ["stopmensonges.com", "0", "0", "0.5"], ["www.breizh-info.com", "0", "0", "0.2"], ["breizatao.com", "0", "0", "0.1"]], "labels": {"name": "Site", "values": ["www.lefigaro.fr", "www.lemonde.fr", "www.francetvinfo.fr", "www.huffingtonpost.fr", "www.20mitues.fr", "francais.rt.com", "fr.sputniknews.com", "www.santeplusmag.com", "www.santenatureinnovation.com", "www.espritsciencemetaphysiques.com", "eddenya.com", "www.letopdelhumour.fr", "www.egaliteetreconciliation.fr", "lagauchematuer.fr", "sante-nutrition.org", "www.topsante.org", "ripostelaique.com", "www.dreuz.info", "lesmoutonsenrages.fr", "resistancerepulicaine.eu", "www.nouvelordremondial.cc", "reseauinternational.net", "lesobervateurs.ch", "lesmoutonsrebelles.com", "stopmensonges.com", "www.breizh-info.com", "breizatao.com"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/our-research/measuring-reach-fake-news-and-online-disinformation-europe", "type": "Problem", "unit": "100k interactions", "year": "2017", "title": "Average Monthly Facebook Interactions for Prominent French News Sites and Some of The Most Popular False News Sites (2017)", "topic": "Disinformation", "method": "Data mining", "source": "Fletcher, Richard, Alessio Cornia, Lucas Graves and Rasmus Kleis. \"Measuring the Reach of “Fake News” and Online Disinformation in Europe,\" Reuters Institute, February 2018", "sub_topic": "Prevalence of disinformation", "chart_number": "25", "geographical": "France"}, "description": "This column chart from the Reuters Institute shows the average monthly Facebook interactions for several prominent French news sites and some of the most popular false French news sites, in million minutes. Notably, although the prominent news sites outperformed the false ones in reach and monthly time spent on their pages, their Facebook interactions lag behind some of the interactions achieved by the false news sites."},
{"data": [{"data": [55.4, 22.4, 19.7, 13.5, 1.6, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], "name": "Prominent"}, {"data": [null, null, null, null, null, 0.9, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], "name": "Russian news sites"}, {"data": [null, null, null, null, null, null, 2.4, 2.4, null, null, null, 1.7, 4.7, 0.8, 3.2, null, 5.8, 0.8, null, 7.2, null, 2.9, null, null, null, null], "name": "False news site"}], "_data": [["Site", "Prominent", "Russian news sites", "False news site"], ["www.republica.it", "55.4", "0", "0"], ["www.corriere.it", "22.4", "0", "0"], ["www.tgcom24.mediase.it", "19.7", "0", "0"], ["www.huffingtonpost.it", "13.5", "0", "0"], ["www.rainews.it", "1.6", "0", "0"], ["it.sputniknews.com", "0", "0.9", "0"], ["www.retenews24.it", "0", "0", "2.4"], ["www.meteoweb.eu", "0", "0", "2.4"], ["www.breaknotizie.com", "0", "0", "0"], ["www.direttanews.it", "0", "0", "0"], ["www.internapoli.it", "0", "0", "0"], ["www.dionidream.com", "0", "0", "1.7"], ["www.sostenitori.info", "0", "0", "4.7"], ["www.meteogiornale.it", "0", "0", "0.8"], ["www.eticamente.net", "0", "0", "3.2"], ["www.inews24.it", "0", "0", "0"], ["www.italiapatriamia.eu", "0", "0", "5.8"], ["tzetze.it", "0", "0", "0.8"], ["www.segnidalcielo.it", "0", "0", "0"], ["www.lovivoaroma.org", "0", "0", "7.2"], ["www.mednat.org", "0", "0", "0"], ["www.ilprimatonazionale.it", "0", "0", "2.9"], ["www.imolaoggi.it", "0", "0", "0"], ["www.eurosalus.com", "0", "0", "0"], ["www.disinformazione.it", "0", "0", "0"], ["www.informasalus.it", "0", "0", "0"]], "labels": {"name": "Site", "values": ["www.republica.it", "www.corriere.it", "www.tgcom24.mediase.it", "www.huffingtonpost.it", "www.rainews.it", "it.sputniknews.com", "www.retenews24.it", "www.meteoweb.eu", "www.breaknotizie.com", "www.direttanews.it", "www.internapoli.it", "www.dionidream.com", "www.sostenitori.info", "www.meteogiornale.it", "www.eticamente.net", "www.inews24.it", "www.italiapatriamia.eu", "tzetze.it", "www.segnidalcielo.it", "www.lovivoaroma.org", "www.mednat.org", "www.ilprimatonazionale.it", "www.imolaoggi.it", "www.eurosalus.com", "www.disinformazione.it", "www.informasalus.it"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/our-research/measuring-reach-fake-news-and-online-disinformation-europe", "type": "Problem", "unit": "Monthly reach (%)", "year": "2017", "title": "Average Monthly Reach of Prominent Italian News Sites and Some of The Most Popular False News Sites (2017)", "topic": "Disinformation", "method": "Data mining", "source": "Fletcher, Richard, Alessio Cornia, Lucas Graves and Rasmus Kleis. \"Measuring the Reach of “Fake News” and Online Disinformation in Europe,\" Reuters Institute, February 2018", "sub_topic": "Prevalence of disinformation", "chart_number": "26", "geographical": "Italy"}, "description": "In general, the news sites included in the sample outperformed the false news outlets with well over one million interactions per month. In addition, La Repubblica outperformed all of the news sites we considered in the sample. However, eight of the 20 false news outlets in this sample generated more interactions per month than the news website of the Italian public broadcaster, Rainews."},
{"data": [{"data": [443.5, 296.6, 90.1, 39.7, 13.9, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], "name": "Prominent"}, {"data": [null, null, null, null, null, 2.1, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], "name": "Russian news sites"}, {"data": [null, null, null, null, null, null, 3.2, 4.3, 3.7, 2.2, 2.3, 1.2, 2.2, 7.5, 0.7, 0.9, 0.6, 1.1, 0.5, null, 0.1, 0.2, 0.1, 0.1, 0.1, 0.1], "name": "False news site"}], "_data": [["Site", "Prominent", "Russian news sites", "False news site"], ["www.republica.it", "443.5", "0", "0"], ["www.corriere.it", "296.6", "0", "0"], ["www.tgcom24.mediase.it", "90.1", "0", "0"], ["www.huffingtonpost.it", "39.7", "0", "0"], ["www.rainews.it", "13.9", "0", "0"], ["it.sputniknews.com", "0", "2.1", "0"], ["www.retenews24.it", "0", "0", "3.2"], ["www.meteoweb.eu", "0", "0", "4.3"], ["www.breaknotizie.com", "0", "0", "3.7"], ["www.direttanews.it", "0", "0", "2.2"], ["www.internapoli.it", "0", "0", "2.3"], ["www.dionidream.com", "0", "0", "1.2"], ["www.sostenitori.info", "0", "0", "2.2"], ["www.meteogiornale.it", "0", "0", "7.5"], ["www.eticamente.net", "0", "0", "0.7"], ["www.inews24.it", "0", "0", "0.9"], ["www.italiapatriamia.eu", "0", "0", "0.6"], ["tzetze.it", "0", "0", "1.1"], ["www.segnidalcielo.it", "0", "0", "0.5"], ["www.lovivoaroma.org", "0", "0", "0"], ["www.mednat.org", "0", "0", "0.1"], ["www.ilprimatonazionale.it", "0", "0", "0.2"], ["www.imolaoggi.it", "0", "0", "0.1"], ["www.eurosalus.com", "0", "0", "0.1"], ["www.disinformazione.it", "0", "0", "0.1"], ["www.informasalus.it", "0", "0", "0.1"]], "labels": {"name": "Site", "values": ["www.republica.it", "www.corriere.it", "www.tgcom24.mediase.it", "www.huffingtonpost.it", "www.rainews.it", "it.sputniknews.com", "www.retenews24.it", "www.meteoweb.eu", "www.breaknotizie.com", "www.direttanews.it", "www.internapoli.it", "www.dionidream.com", "www.sostenitori.info", "www.meteogiornale.it", "www.eticamente.net", "www.inews24.it", "www.italiapatriamia.eu", "tzetze.it", "www.segnidalcielo.it", "www.lovivoaroma.org", "www.mednat.org", "www.ilprimatonazionale.it", "www.imolaoggi.it", "www.eurosalus.com", "www.disinformazione.it", "www.informasalus.it"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/our-research/measuring-reach-fake-news-and-online-disinformation-europe", "type": "Problem", "unit": "Average monthly time spent (million minutes)", "year": "2017", "title": "Average Monthly Time Spent With Prominent Italian News Sites and Some of The Most Popular False News Sites (2017)", "topic": "Disinformation", "method": "Data mining", "source": "Fletcher, Richard, Alessio Cornia, Lucas Graves and Rasmus Kleis. \"Measuring the Reach of “Fake News” and Online Disinformation in Europe,\" Reuters Institute, February 2018", "sub_topic": "Prevalence of disinformation", "chart_number": "27", "geographical": "Italy"}, "description": "The best performing outlet was Meteo Giornale—ostensibly\na weather site, but also one that has been shown to\npublish false information about supposedly imminent\nasteroid strikes and the like. Again, this is roughly half\nthe equivalent figure for Rainews, but very far behind\nthe figures for La Repubblica (443.5 million minutes)\nand Il Corriere della Sera (296.6 million minutes)."},
{"data": [{"data": [287, 263], "name": "Number of engagements"}], "_data": [["Type of article", "Number of engagements"], ["Vote Leave", "287,000"], ["RT/Sputnik", "263,000"]], "labels": {"name": "Type of article", "values": ["Vote Leave", "RT/Sputnik"]}, "metadata": {"link": "https://www.europeanvalues.net/vyzkum/influence-of-russian-disinformation-operations-specific-examples-in-data-and-numbers/", "type": "Problem", "unit": "Number of engagements", "year": "2016", "title": "Number of Engagements Connected to the Clearly Anti-European Union Articles", "topic": "Disinformation", "method": "Data mining", "source": "Špalková, Veronika. \"Influence of Russian Disinformation Operations: Specific Examples in Data and Numbers,\" European Values Think-Tank, 2018", "sub_topic": "Prevalence of disinformation", "chart_number": "29", "geographical": "United Kingdom"}, "description": "The graph illustrates the high number of engagements connected to clearly anti-European Union articles related to the Vote Leave campaign and to Russia Today and Sputnik. It also shows the high level of engagement of Russian disinformation organisations in United Kingdom, in the months leading up to Brexit. The European Union refers to EU28. The United Kindom left the European Union on 31 January 2020."},
{"data": [{"data": [72488, 70457, 52042, 47964], "name": "Shared posts"}], "_data": [["Source", "Shared posts"], ["Spanish eldiario.es", "72488"], ["British BBC", "70457"], ["Spanish El Pasío", "52042"], ["Russian RT/Sputnik", "47964"]], "labels": {"name": "Source", "values": ["Spanish eldiario.es", "British BBC", "Spanish El Pasío", "Russian RT/Sputnik"]}, "metadata": {"link": "https://www.europeanvalues.net/vyzkum/influence-of-russian-disinformation-operations-specific-examples-in-data-and-numbers/", "type": "Problem", "unit": "Number of shared posts", "year": "2017", "title": "Catalan Crisis- Number of Shared Posts", "topic": "Disinformation", "method": "Data mining", "source": "Špalková, Veronika. \"Influence of Russian Disinformation Operations: Specific Examples in Data and Numbers,\" European Values Think-Tank, 2018", "sub_topic": "Prevalence of disinformation", "chart_number": "30", "geographical": "Spain"}, "description": "Naturally, the Spanish media was the most active in\nterms of the number of published articles as well as the\namount of online sharing. However, according to the source, Russian news media (Russia Today/Sputnik) took the fourth place."},
{"data": [{"data": [136.9, 218.2, 208.1, 125.9], "name": "Number of reachable readers and viewers "}], "_data": [["Reachable viewers (millions)", "Number of reachable readers and viewers "], ["Spanish eldiario.es", "136.9"], ["British BBC", "218.2"], ["Spanish El Pasío", "208.1"], ["Russian RT/Sputnik", "125.9"]], "labels": {"name": "Reachable viewers (millions)", "values": ["Spanish eldiario.es", "British BBC", "Spanish El Pasío", "Russian RT/Sputnik"]}, "metadata": {"link": "https://www.europeanvalues.net/vyzkum/influence-of-russian-disinformation-operations-specific-examples-in-data-and-numbers/", "type": "Problem", "unit": "Reachable viewers (millions)", "year": "2017", "title": "Catalan Crisis - Number of Reachable Viewers", "topic": "Disinformation", "method": "Data mining", "source": "Špalková, Veronika. \"Influence of Russian Disinformation Operations: Specific Examples in Data and Numbers,\" European Values Think-Tank, 2018", "sub_topic": "Prevalence of disinformation", "chart_number": "301", "geographical": "Spain"}, "description": "During the Catalan crisis, the Russian news sources (Russia Today and Sputnik) have reached similar level of engagement of the viewers and readers as the Spanish news sources and BBC."},
{"data": [{"data": [26, 36, 22, 16, null], "name": "Per cent of respondents"}], "_data": [["Confidence level", "Per cent of respondents"], ["Very confident", "26"], ["Slightly confident", "36"], ["Not particularly confident", "22"], ["Not at all confident", "16"], ["Don't know", "0"]], "labels": {"name": "Confidence level", "values": ["Very confident", "Slightly confident", "Not particularly confident", "Not at all confident", "Don't know"]}, "metadata": {"link": "https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/729184/oci-tracker.pdf", "type": "Problem", "unit": "Share of respondents", "year": "2018", "title": "United Kingdom Respondents' Confidence in Knowing What is Legal in Accessing Content Online", "topic": "Copyright Infringement", "method": "Survey (N=4573)", "source": "Kantar Media, Intellectual Property Office (UK). Online Copyright Infringement Tracker Wave 8 (London: Intellectual Property Office, 2018)", "sub_topic": "Public knowledge of copyright infringement ", "chart_number": "31", "geographical": "United Kingdom"}, "description": "The chart shows the results of a 2018 survey in the United Kingdom. When asked, \"How confident are you that you know what is legal and what isn't in terms of downloading, streaming/accessing, and sharing content through the internet?\" the majority of respondents answered that they were confident. However, 16% of respondents reported that they were not at all confident in their ability to know what is legal online."},
{"data": [{"data": [62, 36, 19, 19, 16, 16, 13, 12, 10, 9, 8, 6, 4, 3, 3], "name": "Share of respondents"}], "_data": [["Sites or services", "Share of respondents"], ["YouTube", "62"], ["Spotify", "36"], ["Facebook", "19"], ["Amazon Music", "19"], ["Apple Music", "16"], ["Google (search engine)", "16"], ["iTunes/App Store/ibookstore/Apple Store", "13"], ["Amazon/Amazon mp3/Kindle", "12"], ["Soundcloud", "10"], ["Email", "9"], ["Google Play/Android Marketplace", "8"], ["Pirate Bay", "6"], ["Kodi", "4"], ["MP3Skull", "3"], ["BitTorrent software", "3"]], "labels": {"name": "Sites or services", "values": ["YouTube", "Spotify", "Facebook", "Amazon Music", "Apple Music", "Google (search engine)", "iTunes/App Store/ibookstore/Apple Store", "Amazon/Amazon mp3/Kindle", "Soundcloud", "Email", "Google Play/Android Marketplace", "Pirate Bay", "Kodi", "MP3Skull", "BitTorrent software"]}, "metadata": {"link": "https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/729184/oci-tracker.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2018", "title": "Sites and Services Used by United Kingdom Respondents to Access Music Online", "topic": "Copyright Infringement", "method": "Survey (N=396)", "source": "Kantar Media, Intellectual Property Office (UK). Online Copyright Infringement Tracker Wave 8 (London: Intellectual Property Office, 2018)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "32", "geographical": "United Kingdom"}, "description": "The chart shows the results of a 2018 survey in the United Kingdom concerning the online music services and sites used by music copyright infringers. YouTube was by far the most popular way for respondents to stream, access, or share music tracks or albums. Several pirating websites, including Pirate Bay, were mentioned by more than 1% of respondents. "},
{"data": [{"data": [18, 18, 16, 16, 6], "name": "Share of respondents"}], "_data": [["Time", "Share of respondents"], ["2015", "18"], ["2016", "18"], ["2017", "16"], ["2018", "16"], ["2019", "6"]], "labels": {"name": "Time", "values": ["2015", "2016", "2017", "2018", "2019"]}, "metadata": {"link": "https://www.gov.uk/government/publications/online-copyright-infringement-tracker-survey-9th-wave", "type": "Problem", "unit": "Share of respondents", "year": "2019", "title": "United Kingdom Respodents Who Have Consumed Video Games Online Illegally in the Past Three Months", "topic": "Copyright Infringement", "method": "Survey (N=1033)", "source": "Ofcom, GOV.UK, Intellectual Property Office (UK). Online Copyright Infringement Tracker Wave 9 (London: GOV.UK and Intellectual Property Office, 2020)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "33", "geographical": "United Kingdom"}, "description": "The chart shows the results of a several public surveys in the United Kingdom carried out from 2012 to 2019. The share of respondents reporting that they have illegally consumed video games online in the last three months shows a signficant decline in 2019 compared to previous year, decreasing from 16% in 2018 to only 6%."},
{"data": [{"data": [11, 12, 11, 13, 35], "name": "Share of respondents"}], "_data": [["Time", "Share of respondents"], ["2015", "11"], ["2016", "12"], ["2017", "11"], ["2018", "13"], ["2019", "35"]], "labels": {"name": "Time", "values": ["2015", "2016", "2017", "2018", "2019"]}, "metadata": {"link": "https://www.gov.uk/government/publications/online-copyright-infringement-tracker-survey-9th-wave", "type": "Problem", "unit": "Share of respondents", "year": "2019", "title": "United Kingdom Respodents Who Have Consumed e-Books Illegally in the Past Three Months", "topic": "Copyright Infringement", "method": "Survey (N=1066)", "source": "Ofcom, GOV.UK, Intellectual Property Office (UK). Online Copyright Infringement Tracker Wave 9 (London: GOV.UK and Intellectual Property Office, 2020)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "34", "geographical": "United Kingdom"}, "description": "The chart shows the results of a several public surveys in the United Kingdom carried out from 2012 to 2019. The share of respondents reporting that they have illegally consumed e-books in the last three months was almost three times higher in 2019 compared to previous year, increasing from 13% in 2018 to 35%."},
{"data": [{"data": [21, 20, 22, 23, 17], "name": "Share of respondents"}], "_data": [["Time", "Share of respondents"], ["2015", "21"], ["2016", "20"], ["2017", "22"], ["2018", "23"], ["2019", "17"]], "labels": {"name": "Time", "values": ["2015", "2016", "2017", "2018", "2019"]}, "metadata": {"link": "https://www.gov.uk/government/publications/online-copyright-infringement-tracker-survey-9th-wave", "type": "Problem", "unit": "Share of respondents", "year": "2019", "title": "United Kingdom Respondents Who Have Consumed Digital Television Programs Illegally in The Past Three Months", "topic": "Copyright Infringement", "method": "Survey (N=2340)", "source": "Ofcom, GOV.UK, Intellectual Property Office (UK). Online Copyright Infringement Tracker Wave 9 (London: GOV.UK and Intellectual Property Office, 2020)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "35", "geographical": "United Kingdom"}, "description": "The chart shows the results of a several public surveys in the United Kingdom carried out from 2012 to 2019. The share of respondents reporting that they have illegally consumed digital television programs in the last three months fell by 6% in 2019, from 23% in 2018 to only 17%."},
{"data": [{"data": [23, 24, 21, 19, 27], "name": "Share of respondents"}], "_data": [["Time", "Share of respondents"], ["2015", "23"], ["2016", "24"], ["2017", "21"], ["2018", "19"], ["2019", "27"]], "labels": {"name": "Time", "values": ["2015", "2016", "2017", "2018", "2019"]}, "metadata": {"link": "https://www.gov.uk/government/publications/online-copyright-infringement-tracker-survey-9th-wave", "type": "Problem", "unit": "Share of respondents", "year": "2019", "title": "United Kingdom Respondents Who Have Consumed Digital Films Illegally in the Past Three Months", "topic": "Copyright Infringement", "method": "Survey (N=1922)", "source": "Ofcom, GOV.UK, Intellectual Property Office (UK). Online Copyright Infringement Tracker Wave 9 (London: GOV.UK and Intellectual Property Office, 2020)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "36", "geographical": "United Kingdom"}, "description": "The chart shows the results of a several public surveys in the United Kingdom carried out from 2012 to 2019. The share of respondents reporting that they have illegally consumed digital films in the last three months shows a significant growth in 2019, breaking the declining trend from the last few years. In 2019, the share of respondents who illegally consumed digital films increased by 8% compared to 2018."},
{"data": [{"data": [24, 20, 18, 19, 20], "name": "Share of respondents"}], "_data": [["Time", "Share of respondents"], ["2015", "24"], ["2016", "20"], ["2017", "18"], ["2018", "19"], ["2019", "20"]], "labels": {"name": "Time", "values": ["2015", "2016", "2017", "2018", "2019"]}, "metadata": {"link": "https://www.gov.uk/government/publications/online-copyright-infringement-tracker-survey-9th-wave", "type": "Problem", "unit": "Share of respondents", "year": "2019", "title": "United Kingdom Respondents Who Have Consumed Digital Music Illegally in The Past Three Months", "topic": "Copyright Infringement", "method": "Survey (N=2297)", "source": "Ofcom, GOV.UK, Intellectual Property Office (UK). Online Copyright Infringement Tracker Wave 9 (London: GOV.UK and Intellectual Property Office, 2020)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "37", "geographical": "United Kingdom"}, "description": "The chart shows the results of a several public surveys in the United Kingdom carried out from 2012 to 2019. The share of respondents reporting that they have illegally consumed digital music in the last three months remains relatively stable, showing only one per cent increase in 2019 compared to 2018."},
{"data": [{"data": [20, 19, 26, 20, 18], "name": "Share of respondents"}], "_data": [["Time", "Share of respondents"], ["2015", "20"], ["2016", "19"], ["2017", "26"], ["2018", "20"], ["2019", "18"]], "labels": {"name": "Time", "values": ["2015", "2016", "2017", "2018", "2019"]}, "metadata": {"link": "https://www.gov.uk/government/publications/online-copyright-infringement-tracker-survey-9th-wave", "type": "Problem", "unit": "Share of respondents", "year": "2019", "title": "United Kingdom Respondents Who Have Consumed Computer Software Illegally in the Past Three Months", "topic": "Copyright Infringement", "method": "Survey (N=997)", "source": "Ofcom, GOV.UK, Intellectual Property Office (UK). Online Copyright Infringement Tracker Wave 9 (London: GOV.UK and Intellectual Property Office, 2020)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "38", "geographical": "United Kingdom"}, "description": "The chart shows the results of a several public surveys in the United Kingdom carried out from 2012 to 2019. The share of respondents reporting that they have illegally consumed computer software in the last three months shows a decline of 2% in 2019 compared to 2018."},
{"data": [{"data": [6, 6, 6, 6, 6, 7, 7], "name": "100% legal (non-infringers)"}, {"data": [35, 35, 32, 26, 23, 24, 19], "name": "Any illegal (infringers)"}], "_data": [["Time", "100% legal (non-infringers)", "Any illegal (infringers)"], ["August to October 2012", "6", "35"], ["November 2012 to January 2013", "6", "35"], ["March to May 2013", "6", "32"], ["March to May 2015", "6", "26"], ["March to May 2016", "6", "23"], ["March to May 2017", "7", "24"], ["March to May 2018", "7", "19"]], "labels": {"name": "Time", "values": ["August to October 2012", "November 2012 to January 2013", "March to May 2013", "March to May 2015", "March to May 2016", "March to May 2017", "March to May 2018"]}, "metadata": {"link": "https://www.gov.uk/government/publications/online-copyright-infringement-tracker-survey-8th-wave", "type": "Problem", "unit": "Share of respondents", "year": "2012-2018", "title": "Use of Peer-to-Peer Services to Consume or Share Digital Content in the United Kingdom", "topic": "Copyright Infringement", "method": "Survey (N=2142)", "source": "Kantar Media, Intellectual Property Office (UK). Online Copyright Infringement Tracker Wave 8 (London: Intellectual Property Office, 2018)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "39", "geographical": "United Kingdom"}, "description": "The chart shows the results of a several public surveys in the United Kingdom between 2012 and 2018. The percent of respondents reporting that they have used peer-to-peer services to consume or share digital content has shown a bit of a downward trend, while the percent of respondents reporting that they never do so has remained fairly constant."},
{"data": [{"data": [8, 9, 7, 19, 10, 4, 4], "name": "Percent of respondents for both"}, {"data": [21, 22, 19, 42, 30, 10, null], "name": "Percent of respondents for one"}], "_data": [["Group", "Percent of respondents for both", "Percent of respondents for one"], ["Total", "8", "21"], ["Men", "9", "22"], ["Women", "7", "19"], ["16-29 year olds", "19", "42"], ["30-44 year olds", "10", "30"], ["45-59 year olds", "4", "10"], ["60-79 year olds", "4", "0"]], "labels": {"name": "Group", "values": ["Total", "Men", "Women", "16-29 year olds", "30-44 year olds", "45-59 year olds", "60-79 year olds"]}, "metadata": {"link": "https://ftvsblogg.files.wordpress.com/2015/06/lc3a4gesrapport-digital-marknad-fc3b6r-film-och-tv.pdf", "type": "Problem", "unit": "Share of respondents", "year": "2015", "title": "Percentage of Swedish Respondents Who Use Illegal Services to Stream or Download Films and/or Series", "topic": "Copyright Infringement", "method": "Survey (N=1003)", "source": "Film- och TV-branschens Samarbetskommitté (FTVS). “Lägesrapport - Digital Marknad för Film och TV” published on FTVS blogg, 03 June 2015", "sub_topic": "Prevalence of copyright infringement", "chart_number": "40", "geographical": "Sweden"}, "description": "The chart, based on survey data from Sweden in 2015, shows that roughly 8% of Swedes report that they use illegal services to download or stream both films and series. Swedish women may be slightly less likely than men to use these illegal services to access films and series."},
{"data": [{"data": [17.1, 12.4, 29.5], "name": "Before HADOPI (%)"}, {"data": [14.6, 15.8, 30.3], "name": "After HADOPI (%)"}], "_data": [["Type of copyright infringement", "Before HADOPI (%)", "After HADOPI (%)"], ["Peer to peer file sharing", "17.1", "14.6"], ["Other platforms", "12.4", "15.8"], ["Total", "29.5", "30.3"]], "labels": {"name": "Type of copyright infringement", "values": ["Peer to peer file sharing", "Other platforms", "Total"]}, "metadata": {"link": "https://hal.archives-ouvertes.fr/hal-01382009/document", "type": "Solution", "unit": "Per cent (%)", "year": "2010", "title": "Evolution of the Sources of Copyright Infringement Before and After Haute Autorité Française pour la Diffusion des Oeuvres et la Protection des Droits sur Internet", "topic": "Copyright Infringement", "method": "Administrative data", "source": "De Filippi, Primavera and Danièle Bourcier. \"Three-Strikes Response to Copyright Infringement: The Case of HADOPI,\" The Turn to Infrastructure in Internet Governance, 2016", "sub_topic": "Prevalence of copyright infringement", "chart_number": "41", "geographical": "France"}, "description": "The chart shows the prevalence of copyright infringement through peer to peer filesharing and other platforms before and after establishment of Haute Autorité Française pour la Diffusion des Oeuvres et la Protection des Droits sur Internet (HADOPI). Although peer to peer file sharing decreased by 15%, overall copyright infringement increased by 3%."},
{"data": [{"data": [146, 185, 189, 190, 191], "name": "eBook Video"}, {"data": [52, 76, 84, 85, 86], "name": "VOD and SVOD"}, {"data": [52, 54, 54, 54, 54], "name": "Catch-up TV"}, {"data": [37, 42, 43, 44, 44], "name": "Music"}, {"data": [27, 30, 28, 28, 28], "name": "Game"}, {"data": [17, 18, 19, 19, 19], "name": "Photo"}, {"data": [6, 7, 7, 5, 5], "name": "Crowdfunding"}], "_data": [["Number of indexed sources", "eBook Video", "VOD and SVOD", "Catch-up TV", "Music", "Game", "Photo", "Crowdfunding"], ["2013", "146", "52", "52", "37", "27", "17", "6"], ["2014", "185", "76", "54", "42", "30", "18", "7"], ["2015", "189", "84", "54", "43", "28", "19", "7"], ["2016", "190", "85", "54", "44", "28", "19", "5"], ["2017", "191", "86", "54", "44", "28", "19", "5"]], "labels": {"name": "Number of indexed sources", "values": ["2013", "2014", "2015", "2016", "2017"]}, "metadata": {"link": "https://www.hadopi.fr/sites/default/files/sites/default/files/ckeditor_files/Activity-report-2016-17-HADOPI.pdf", "type": "Solution", "unit": "Number of indexed sources", "year": "2017", "title": "Applications for the Labelling of the Legal Offers Sent to the High Authority for the Dissemination of Works and the Protection of Rights on the Internet (HADOPI) Through Labelling or Indexing", "topic": "Copyright Infringement", "method": "Administrative data", "source": "French High Authority for the Dissemination of Works and the Protection of Rights on the Internet. Activity Report (Paris: HADOPI, 2017)", "sub_topic": "Effectiveness of measures to combat copyright infringement", "chart_number": "42", "geographical": "France"}, "description": "HADOPI has drawn up a catalogue of indexed offers that do not infringe intellectual property rights. It reports this on the www.offrelegale.fr portal. This is a tool available to users to search for platforms based on their access (streaming/download) or consumption (pay-per-view/subscription) preferences. As of 30 September 2017, HADOPI has 427 cultural sites and services indexed on www.offrelegale.fr. "},
{"data": [{"data": [1342, 183530, 236173, 925], "name": "Total reported, without removed items"}, {"data": [362, 58297, 28645, 332], "name": "Removed items"}], "_data": [["Platform", "Total reported, without removed items", "Removed items"], ["Facebook", "1342", "362", "", "1704"], ["Google (YouTube)", "183530", "58297", "", "241827"], ["Twitter", "236173", "28645", "", "264818"], ["Change.org", "925", "332", "", "1257"]], "labels": {"name": "Platform", "values": ["Facebook", "Google (YouTube)", "Twitter", "Change.org"]}, "metadata": {"link": "https://www.ivir.nl/publicaties/download/NetzDG_Tworek_Leerssen_April_2019.pdf", "type": "Solution", "unit": "Per cent (%)", "year": "2018", "title": "Overview of the Number of Reported Items by Platform in Germany (2018)", "topic": "Illegal Content", "method": "Data mining", "source": "Tworek, Heidi and Paddy Leerssen. \"An Analysis of Germany’s NetzDG Law, \" Transatlantic Working Group, 15 April 2019", "sub_topic": "Removal of illegal content", "chart_number": "44", "geographical": "Germany"}, "description": "The chart presents the data reported by tech companies under the Germany’s Network Enforcement Act about the number of items reported and removed in 2018. The data do not account for other removals based on other types of complaints, referrals, or injunctions."},
{"data": [{"data": [1027, 3206, 292, 66, 1388, 6436, 507], "name": "Removed under NetzDG"}, {"data": [3563, 8695, 2764, 9063, 924, 13499, 3214], "name": "Removed under Google's community guidelines"}], "_data": [["Grounds for removal", "Removed under NetzDG", "Removed under Google's community guidelines"], ["Privacy", "1027", "3563"], ["Defamation or insults", "3206", "8695"], ["Harmful or dangerous acts", "292", "2764"], ["Sexual content", "66", "9063"], ["Terrorist or unconstitutional content", "1388", "924"], ["Hate speech or political extremism", "6436", "13499"], ["Violence", "507", "3214"]], "labels": {"name": "Grounds for removal", "values": ["Privacy", "Defamation or insults", "Harmful or dangerous acts", "Sexual content", "Terrorist or unconstitutional content", "Hate speech or political extremism", "Violence"]}, "metadata": {"link": "https://www.ivir.nl/publicaties/download/NetzDG_Tworek_Leerssen_April_2019.pdf", "type": "Solution", "unit": "Number of items removed", "year": "2018", "title": "Content Removal Comparison: Google Community Guidelines vs. Germany’s Network Enforcement Act (2018)", "topic": "Illegal Content", "method": "Self-reporting", "source": "Tworek, Heidi and Paddy Leerssen. \"An Analysis of Germany’s NetzDG Law, \" Transatlantic Working Group, 15 April 2019", "sub_topic": "Removal of illegal content", "chart_number": "45", "geographical": "Germany"}, "description": "The chart presents the distribution of posts removed by Google due to violations of Google's community guidelines and the Germany’s Network Enforcement Act, on the grounds for removal, for the period July - December 2018. The data shows that the majority of removal decisions are based on the platform’s private standards, as they often prioritise the compliance with their community guidelines, and not with German speech laws."},
{"data": [{"data": [13, 2], "name": "Daily"}, {"data": [16, 7], "name": "Weekly"}, {"data": [8, 6], "name": "Monthly"}], "_data": [["Frequency", "Daily", "Weekly", "Monthly"], ["Streaming movies and/or television series (37%)", "13", "16", "8"], ["Downloading movies and/or television series (15%)", "2", "7", "6"]], "labels": {"name": "Frequency", "values": ["Streaming movies and/or television series (37%)", "Downloading movies and/or television series (15%)"]}, "metadata": {"link": "http://danske-biografer.dk/halvdelen-af-de-danske-unge-streamer-ulovligt/", "type": "Problem", "unit": "Share of respondents", "year": "2016", "title": "Share of Individuals Who Have Streamed or Downloaded Films or Television Series From Potential Illegal Websites in Denmark, by Frequency (2016)", "topic": "Copyright Infringement", "method": "Survey (N=1506)", "source": "Michael Carving. “Halvdelen af de Danske Unge Streamer Ulovligt,” published in Danske Biografer, 22 November 2016", "sub_topic": "Prevalence of copyright infringement", "chart_number": "46", "geographical": "Denmark"}, "description": "The chart displays the share of individuals who have streamed or downloaded films or TV series from potential illegal websites in Denmark, based on a survey carried out in 2016. A significant number of individuals reported that they regularly stream films or television series from potentially illegal websites, with fewer people reporting that they regularly download films or television series from potentially illegal websites."},
{"data": [{"data": [0.35, 0.7, 0.82, 0.93, 1, 1, 1], "name": "All suspended accounts"}, {"data": [0.05, 0.12, 0.36, 0.8, 1, 1, 1], "name": "ISIS accounts"}], "_data": [["Number of tweets before suspension", "All suspended accounts", "ISIS accounts"], ["1", "0.35", "0.05"], ["10", "0.7", "0.12"], ["100", "0.82", "0.36"], ["1000", "0.93", "0.8"], ["10000", "1", "1"], ["100000", "1", "1"], ["1000000", "1", "1"]], "labels": {"name": "Number of tweets before suspension", "values": ["1", "10", "100", "1000", "10000", "100000", "1000000"]}, "metadata": {"link": "https://snap.stanford.edu/mis2/files/MIS2_paper_23.pdf", "type": "Problem", "unit": "Share of suspended users", "year": "2015", "title": "ISIS Accounts Spread More Content Before Getting Suspended by Twitter Compared With Other Eventually Suspended Accounts", "topic": "Incitement to Terrorism", "method": "Data mining", "source": "Alfifi, Majid, Parisa Kaghazgaran and James Caverlee. \"Measuring the Impact of ISIS Social Media Strategy, \" Department of Computer Science and Engineering, Texas A&M University, 2015", "sub_topic": "Prevalence of incitement to terrorism", "chart_number": "47", "geographical": "Global"}, "description": "This graph shows the percentage of suspended users who were able to tweet 10, 100, 1,000, or more times before being suspended. Based on data collected from Twitter, this chart shows that ISIS accounts seem to successfully tweet more posts before being suspended, compared to the entire population of suspended accounts."},
{"data": [{"data": [23880, 551869, 745721, 23880, 1753195, 2161106], "name": "Accounts"}, {"data": [17434323, 10436603, 19570380, 17454068, 12175619, 17479990], "name": "Tweets"}], "_data": [["Dataset", "Accounts", "Tweets"], ["ISIS Tweets", "23880", "17434323"], ["ISIS Retweets", "551869", "10436603"], ["ISIS Mentions", "745721", "19570380"], ["Legit Tweets", "23880", "17454068"], ["Legit Retweets", "1753195", "12175619"], ["Legit Mentions", "2161106", "17479990"]], "labels": {"name": "Dataset", "values": ["ISIS Tweets", "ISIS Retweets", "ISIS Mentions", "Legit Tweets", "Legit Retweets", "Legit Mentions"]}, "metadata": {"link": "https://snap.stanford.edu/mis2/files/MIS2_paper_23.pdf", "type": "Problem", "unit": "Number of tweets before suspension", "year": "2015", "title": "Retweets of ISIS Accounts", "topic": "Incitement to Terrorism", "method": "Data mining", "source": "Alfifi, Majid, Parisa Kaghazgaran and James Caverlee. \"Measuring the Impact of ISIS Social Media Strategy, \" Department of Computer Science and Engineering, Texas A&M University, 2015", "sub_topic": "Prevalence of incitement to terrorism", "chart_number": "48", "geographical": "Global"}, "description": "The chart compares the number of tweets, retweets, and mentions achieved by ISIS accounts compared to a randomly sampled set of users, based on data collected from Twitter. The random sample of users received significantly more retweets and mentions per account. The random sample also received more retweets and mentions per tweet, but the difference there was much smaller."},
{"data": [{"data": [1760, 1347, 1609], "name": "0-2 years old"}, {"data": [10912, 9080, 15119], "name": "3-6 years old"}, {"data": [30217, 30156, 45744], "name": "7-10 years old"}, {"data": [31517, 58007, 63533], "name": "11-13 years old"}, {"data": [2249, 4732, 4450], "name": "14-15 years old"}, {"data": [284, 207, 460], "name": "16-17 years old"}], "_data": [["Age ", "0-2 years old", "3-6 years old", "7-10 years old", "11-13 years old", "14-15 years old", "16-17 years old"], ["2017", "1760", "10912", "30217", "31517", "2249", "284", "", "76939"], ["2018", "1347", "9080", "30156", "58007", "4732", "207", "", "103529"], ["2019", "1609", "15119", "45744", "63533", "4450", "460", "", "130915", "26.45%", "70.15%"]], "labels": {"name": "Age ", "values": ["2017", "2018", "2019"]}, "metadata": {"link": "https://www.iwf.org.uk/report/iwf-2019-annual-report-zero-tolerance", "type": "Problem", "unit": "Number of webpages", "year": "2017-2019", "title": "Number of Web Pages containing Adverts or Links to Child Sexual Abuse Material", "topic": "Illegal Content", "method": "Data collection", "source": "The Internet Watch Foundation. Annual Report 2019 (Cambridge: The Internet Watch Foundation, 2019)", "sub_topic": "Prevalence of illegal content", "chart_number": "49", "geographical": "Global"}, "description": "The chart provides information on the number of web pages containing adverts or links to child sexual abuse imagery, according to the age of children. The data shows an increase of these web pages in 2019 by 26% compared to 2018 and  by 70% compared to 2017."},
{"data": [{"data": [93962, 7644, 6280, 5466, 4121, 3905, 1989, 1624, 1298, 1237], "name": "Number of Web Pages"}], "_data": [["Country", "Number of Web Pages"], ["Netherlands", "93962"], ["Slovakia", "7644"], ["United States", "6280"], ["Canada", "5466"], ["Russia", "4121"], ["France", "3905"], ["Latvia", "1989"], ["Thailand", "1624"], ["Luxembourg", "1298"], ["Romania", "1237"]], "labels": {"name": "Country", "values": ["Netherlands", "Slovakia", "United States", "Canada", "Russia", "France", "Latvia", "Thailand", "Luxembourg", "Romania"]}, "metadata": {"link": "https://www.iwf.org.uk/report/iwf-2019-annual-report-zero-tolerance", "type": "Problem", "unit": "Number of web pages", "year": "2019", "title": "The Top Ten Countries for Hosting Child Sexual Abuse Content", "topic": "Illegal Content", "method": "Data collection", "source": "The Internet Watch Foundation. Annual Report 2019 (Cambridge: The Internet Watch Foundation, 2019)", "sub_topic": "Prevalence of illegal content", "chart_number": "49", "geographical": "Global"}, "description": "The chart shows the top 10 countries that host web pages with child sexual abuse material, based on the assessment of the Internet Watch Foundation. Interestingly, seven out of 10 countries are in Europe and six out of 10 are in the European Union."},
{"data": [{"data": [26, 33, 35, 37, 40, 41, 46, 49, 49, 51, 58, 62, 63, 63, 64, 64, 65, 66, 67, 69], "name": "Islamic State"}, {"data": [null, 1, 2, 5, 6, 7, 8, 12, 13, 13, 14, 15, 17, 18, 20, 20, 21, 21, 22, 22], "name": "Other Jihadi"}], "_data": [["Cummulative suspension rate (%)", "Islamic State", "Other Jihadi"], ["5", "26", "0"], ["10", "33", "1"], ["15", "35", "2"], ["20", "37", "5"], ["25", "40", "6"], ["30", "41", "7"], ["35", "46", "8"], ["40", "49", "12"], ["45", "49", "13"], ["50", "51", "13"], ["55", "58", "14"], ["60", "62", "15"], ["65", "63", "17"], ["70", "63", "18"], ["75", "64", "20"], ["80", "64", "20"], ["85", "65", "21"], ["90", "66", "21"], ["95", "67", "22"], ["100", "69", "22"]], "labels": {"name": "Cummulative suspension rate (%)", "values": ["5", "10", "15", "20", "25", "30", "35", "40", "45", "50", "55", "60", "65", "70", "75", "80", "85", "90", "95", "100"]}, "metadata": {"link": "https://www.tandfonline.com/doi/full/10.1080/1057610X.2018.1513984", "type": "Solution", "unit": "Number of days account survived (binned every five days)", "year": "2017", "title": "Cumulative Suspension Rate For All Accounts in Database", "topic": "Incitement to Terrorism", "method": "Data mining", "source": "Conway, Maura, Moign Khawaja, Suraj Lakhani, Jeremy Reffin, Andrew Robertson and David Weir. “Disrupting Daesh: Measuring Takedown of Online Terrorist Material and Its Impacts,” Studies in Conflict and Terrorism, 29 October 2018", "sub_topic": "Removal of terrorist content", "chart_number": "53", "geographical": "Global"}, "description": "The graph shows the cumulative suspension rate for all accounts identified as being Islamic State or Jihadi by the number of days the accounts survived before being suspended. The data shows that Islamic State accounts have had higher suspension rates compared to other Jihadi accounts."},
{"data": [{"data": [40, 50, 54, 57, 60, 63, 67, 70, 72, 74, 82, 83, 83, 83, 83, 83, 83, 84, 84, 84], "name": "Islamic State"}, {"data": [1, 3, 5, 11, 14, 17, 21, 30, 33, 35, 37, 39, 42, 42, 44, 44, 44, 44, 44, 45], "name": "Other Jihadi"}], "_data": [["Number of days account survived (binned every five days)", "Islamic State", "Other Jihadi"], ["5", "40", "1"], ["10", "50", "3"], ["15", "54", "5"], ["20", "57", "11"], ["25", "60", "14"], ["30", "63", "17"], ["35", "67", "21"], ["40", "70", "30"], ["45", "72", "33"], ["50", "74", "35"], ["55", "82", "37"], ["60", "83", "39"], ["65", "83", "42"], ["70", "83", "42"], ["75", "83", "44"], ["80", "83", "44"], ["85", "83", "44"], ["90", "84", "44"], ["95", "84", "44"], ["100", "84", "45"]], "labels": {"name": "Number of days account survived (binned every five days)", "values": ["5", "10", "15", "20", "25", "30", "35", "40", "45", "50", "55", "60", "65", "70", "75", "80", "85", "90", "95", "100"]}, "metadata": {"link": "https://www.tandfonline.com/doi/full/10.1080/1057610X.2018.1513984", "type": "Solution", "unit": "Cumulative suspension rate (%)", "year": "2017", "title": "Cumulative Suspension Rate for Accounts Eventually Suspended", "topic": "Incitement to Terrorism", "method": "Data mining", "source": "Conway, Maura, Moign Khawaja, Suraj Lakhani, Jeremy Reffin, Andrew Robertson and David Weir. “Disrupting Daesh: Measuring Takedown of Online Terrorist Material and Its Impacts,” Studies in Conflict and Terrorism, 29 October 2018", "sub_topic": "Removal of terrorist content", "chart_number": "54", "geographical": "Global"}, "description": "The graph shows the cumulative suspension rate for all accounts identified as being Islamic State or Jihadi by the number of days the accounts survived before being suspended (on the horizontal axis). The data shows that Islamic State accounts have had higher suspension rates compared to other Jihadi accounts. The chart focuses on accounts that were independently judged to have breached Twitter’s terms of service."},
{"data": [{"data": [1330, null, 472, 792, 431, 410, 353, null, null, 243, null, null, 198, null, 155, 139, null, null], "name": "Pro-Islamic State Accounts"}, {"data": [2488, 1294, 479, null, null, null, null, 316, 244, null, 228, 208, null, 189, null, null, 111, 103], "name": "Other jihadist accounts"}], "_data": [["Platform (Pro-IS)", "Pro-Islamic State Accounts", "Other jihadist accounts"], ["YouTube", "1330", "2488", "3818"], ["Facebook", "0", "1294", "1294"], ["justpaste.it", "472", "479", "951"], ["Google Drive", "792", "0", "792"], ["Google Photos", "431", "0", "431"], ["sendvid.com", "410", "0", "410"], ["archive.org", "353", "0", "353"], ["Islamic prayers website", "0", "316", "316"], ["Taliban news website", "0", "244", "244"], ["archive.is", "243", "0", "243"], ["Official Taliban website", "0", "228", "228"], ["Taliban's official Urdu language website", "0", "208", "208"], ["\"Unofficial\" Bahasa language IS fan site", "198", "0", "198"], ["Hizb ut-Tahrir website", "0", "189", "189"], ["medium.com", "155", "0", "155"], ["\"Unofficial\" Arabic language IS news site", "139", "0", "139"], ["Telegram", "0", "111", "111"], ["Taliban's official English language website", "0", "103", "103"]], "labels": {"name": "Platform (Pro-IS)", "values": ["YouTube", "Facebook", "justpaste.it", "Google Drive", "Google Photos", "sendvid.com", "archive.org", "Islamic prayers website", "Taliban news website", "archive.is", "Official Taliban website", "Taliban's official Urdu language website", "\"Unofficial\" Bahasa language IS fan site", "Hizb ut-Tahrir website", "medium.com", "\"Unofficial\" Arabic language IS news site", "Telegram", "Taliban's official English language website"]}, "metadata": {"link": "https://www.tandfonline.com/doi/full/10.1080/1057610X.2018.1513987 ", "type": "Solution", "unit": "Number of accounts", "year": "2017", "title": "Platforms Linked to Islamic State and Jihadist Accounts (Based on Out-Links from Twitter)", "topic": "Incitement to Terrorism", "method": "Data collection", "source": "Conway, Maura, Moign Khawaja, Suraj Lakhani, Jeremy Reffin, Andrew Robertson and David Weir. “Disrupting Daesh: Measuring Takedown of Online Terrorist Material and Its Impacts,” Studies in Conflict and Terrorism, 29 October 2018", "sub_topic": "Removal of terrorist content", "chart_number": "56", "geographical": "Global"}, "description": "This graph shows the top 10 platforms linked to Islamic State accounts and top 10 platforms linked to other Jihadist accounts on Twitter. The data shows that YouTube was the preferred platform for both types of accounts. Interestingly, Facebook was not in the top 10 for Islamic State accounts but was number 2 for other Jihadist accounts."},
{"data": [{"data": [38, 33, 28, 44, 35, 43], "name": "Social media"}, {"data": [22, 23, 23, 32, 33, 27], "name": "Video sites"}, {"data": [22, 24, 22, 44, 31, 45], "name": "Messaging apps"}, {"data": [14, 17, 16, 24, 22, 19], "name": "Search engines"}], "_data": [["Country", "Social media", "Video sites", "Messaging apps", "Search engines"], ["United Kingdom", "38", "22", "22", "14"], ["United States", "33", "23", "24", "17"], ["Germany", "28", "23", "22", "16"], ["Spain", "44", "32", "44", "24"], ["South Korea", "35", "33", "31", "22"], ["Argentina", "43", "27", "45", "19"]], "labels": {"name": "Country", "values": ["United Kingdom", "United States", "Germany", "Spain", "South Korea", "Argentina"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2020-04/Navigating%20the%20Coronavirus%20Infodemic%20FINAL.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2020", "title": "Respondents’ Perceptions of Channels With False or Misleading Information About Coronavirus", "topic": "Disinformation", "method": "Survey (United Kingdom (N=2216), United States (N=1273), Germany (N=2003), Spain (N=1018), South Korea (N=1009), Argentina (N=1003))", "source": "Nielsen, Rasmus Kleis, Richard Fletcher, Nic Newman, J. Scott Brennen, and Philip N. Howard. \"Navigating the ‘Infodemic’: How People in Six Countries Access and Rate News and Information About Coronavirus,\"  Reuters Institute, April 2020", "sub_topic": "Prevalence of disinformation", "chart_number": "57", "geographical": "Global"}, "description": "The chart presents the distribution of different channels (social media, video sites etc.) where respondents have seen \"a lot\" or \"a great deal\" of false or misleading information about coronavirus. The participants in six countries have answered to the following question \"Q4: How much false or misleading information about coronavirus (COVID-19), if any, do you think you have sen on each of the following in the last week?\" Social media, messaging apps and video sites have been found the main sources of false or misleading information. "},
{"data": [{"data": [35, 28, 24, 42, 37, 47], "name": "People I don't know"}, {"data": [18, 37, 19, 43, 41, 26], "name": "Politicians"}, {"data": [18, 29, 16, 36, 24, 30], "name": "News organisations"}, {"data": [15, 34, 15, 34, 20, 21], "name": "Government"}, {"data": [18, 19, 16, 32, 17, 31], "name": "People I know"}, {"data": [5, 19, 11, 12, 21, 11], "name": "Global health organisations"}, {"data": [5, 15, 10, 18, 12, 17], "name": "National organisations"}, {"data": [6, 13, 11, 13, 12, 13], "name": "Scientists, doctors, experts"}], "_data": [["Country", "People I don't know", "Politicians", "News organisations", "Government", "People I know", "Global health organisations", "National organisations", "Scientists, doctors, experts"], ["United Kingdom", "35", "18", "18", "15", "18", "5", "5", "6"], ["United States", "28", "37", "29", "34", "19", "19", "15", "13"], ["Germany", "24", "19", "16", "15", "16", "11", "10", "11"], ["Spain", "42", "43", "36", "34", "32", "12", "18", "13"], ["South Korea", "37", "41", "24", "20", "17", "21", "12", "12"], ["Argentina", "47", "26", "30", "21", "31", "11", "17", "13"]], "labels": {"name": "Country", "values": ["United Kingdom", "United States", "Germany", "Spain", "South Korea", "Argentina"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2020-04/Navigating%20the%20Coronavirus%20Infodemic%20FINAL.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2020", "title": "Respondents’ Perceptions of Sources With False or Misleading Information About Coronavirus", "topic": "Disinformation", "method": "Survey (United Kingdom (N=2216), United States (N=1273), Germany (N=2003), Spain (N=1018), South Korea (N=1009), Argentina (N=1003))", "source": "Nielsen, Rasmus Kleis, Richard Fletcher, Nic Newman, J. Scott Brennen, and Philip N. Howard. \"Navigating the ‘Infodemic’: How People in Six Countries Access and Rate News and Information About Coronavirus,\"  Reuters Institute, April 2020", "sub_topic": "Prevalence of disinformation", "chart_number": "58", "geographical": "Global"}, "description": "The chart presents the distribution of different sources (politicians, governments etc.) from which respondents have seen \"a lot\" or \"a great deal\" of false or misleading information about coronavirus. The participants in six countries have answered to the following question \"Q4: How much false or misleading information about coronavirus (COVID-19), if any, do you think you have sen on each of the following in the last week?\" "},
{"data": [{"data": [6, 8, 21, 40, 50], "name": "0-9 views "}, {"data": [24, 27, 24, 20, 25], "name": "10-99 views "}, {"data": [70, 65, 55, 40, 25], "name": "100+ views "}], "_data": [["Time period", "0-9 views ", "10-99 views ", "100+ views "], ["2017 Q1", "6", "24", "70"], ["2017 Q2", "8", "27", "65"], ["2017 Q3", "21", "24", "55"], ["2017 Q4", "40", "20", "40"], ["2018 Q1", "50", "25", "25"]], "labels": {"name": "Time period", "values": ["2017 Q1", "2017 Q2", "2017 Q3", "2017 Q4", "2018 Q1"]}, "metadata": {"link": "https://youtube.googleblog.com/2018/04/more-information-faster-removals-more.html", "type": "Solution", "unit": "Per cent (%)", "year": "2018", "title": "Per cent of Videos Taken Down for Extremist Content by Views at Takedown", "topic": "Incitement to Terrorism", "method": "Self-reporting", "source": "YouTube. Per Cent of Videos Taken Down for Extremist Content by Views at Takedown (www.youtube.com, 2018)", "sub_topic": "Removal of terrorist content", "chart_number": "59", "geographical": "Global"}, "description": "The chart shows that the fraction of removed videos which receive less than 100 views before being removed has increased significantly and consistently since the first quarter of 2017. As the original source does not provide explicit data, this chart presents approximate values."},
{"data": [{"data": [45, 28, null, 69, 71, 47, 38], "name": "2014 (% of internet population)"}, {"data": [40, 29, 37, 52, 64, 38, 36], "name": "2017 (% of internet population)"}], "_data": [["Country", "2014 (% of internet population)", "2017 (% of internet population)"], ["France", "45", "40"], ["Germany", "28", "29"], ["Netherlands", "", "37"], ["Poland", "69", "52"], ["Spain", "71", "64"], ["Sweden", "47", "38"], ["Great Britain", "38", "36"]], "labels": {"name": "Country", "values": ["France", "Germany", "Netherlands", "Poland", "Spain", "Sweden", "Great Britain"]}, "metadata": {"link": "https://www.ivir.nl/publicaties/download/Global-Online-Piracy-Study.pdf", "type": "Problem", "unit": "Per cent (%) Note: exclusive of streamripping and pirated copies on physical carriers. ", "year": "2017", "title": "Acquired or Accessed Any Content Type Illegally (2017)", "topic": "Illegal Content", "method": "Survey (N=18578)", "source": "Institute for Information Law of the University of Amsterdam. Global Online Piracy Study (Amsterdam: Institute for Information Law of the University of Amsterdam, 2018)", "sub_topic": "Prevalence of illegal content", "chart_number": "60", "geographical": "European Union"}, "description": "The chart shows the percent of respondents that use the internet who acquired or accessed any type of content illegaly over the past year. Respondents from Poland and Spain were the most likely to report having done so among European Union countries. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020. As a note, the data in the chart covers exclusively the streamripping and pirated copies on physical carriers. "},
{"data": [{"data": [30, 18, null, 50, 51, 24, 25], "name": "2014 (% of internet pop.)"}, {"data": [27, 17, 21, 28, 44, 18, 19], "name": "2017 (% of internet pop.)"}, {"data": [23, 15, 19, 21, 35, 17, 18], "name": "2017 (% of total pop.)"}], "_data": [["Country", "2014 (% of internet pop.)", "2017 (% of internet pop.)", "2017 (% of total pop.)"], ["France", "30", "27", "23"], ["Germany", "18", "17", "15"], ["Netherlands", "", "21", "19"], ["Poland", "50", "28", "21"], ["Spain", "51", "44", "35"], ["Sweden", "24", "18", "17"], ["United Kingdom", "25", "19", "18"]], "labels": {"name": "Country", "values": ["France", "Germany", "Netherlands", "Poland", "Spain", "Sweden", "United Kingdom"]}, "metadata": {"link": "https://www.ivir.nl/publicaties/download/Global-Online-Piracy-Study.pdf", "type": "Problem", "unit": "Percent (%)", "year": "2017", "title": "Consumed Recorded Music From Any Illegal Channel (2017)", "topic": "Copyright Infringement", "method": "Survey (N=18578)", "source": "Institute for Information Law of the University of Amsterdam. Global Online Piracy Study (Amsterdam: Institute for Information Law of the University of Amsterdam, 2018)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "62", "geographical": "European Union"}, "description": "The chart shows the per cent of respondents who use the internet who reported consuming recorded music from any illegal channel during the last year, along with the per cent of all respondents who reported doing so. The chart reveals that music consumption from illegal channels is most popular in Spain, where 35% of the total population engaged in such activity in 2017. The abbreviation \"pop.\" stands for \"population.\" European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [2.4, 1.4, 1.4, 2.2, 4.8, 0.7, 0.9], "name": "Downloads, illegal sources (albums)"}, {"data": [2.4, 1.3, 1.3, 1.9, 5, 1, 1.4], "name": "Streaming, illegal sources (hours)"}, {"data": [1.1, 1.2, 1.2, 1.6, 2.3, 0.6, 1.4], "name": "Streaming, technical devices (h)"}, {"data": [3.4, 2.2, 1.6, 2.4, 4.1, 1.9, 1.9], "name": "Listen streamripped music (h)"}, {"data": [2.7, 1.7, 1.7, 2.5, 5.5, 0.8, 1.2], "name": "Digital illegal (albums)"}, {"data": [1.2, 0.7, null, -0.5, 1.4, -0.2, -0.4], "name": "Digital illegal (albums) -Nominal growth between 2014 and 2017"}], "_data": [["Year-2017", "Downloads, illegal sources (albums)", "Streaming, illegal sources (hours)", "Streaming, technical devices (h)", "Listen streamripped music (h)", "Digital illegal (albums)", "Digital illegal (albums) -Nominal growth between 2014 and 2017"], ["France", "2.4", "2.4", "1.1", "3.4", "2.7", "1.2"], ["Germany", "1.4", "1.3", "1.2", "2.2", "1.7", "0.7"], ["Netherlands", "1.4", "1.3", "1.2", "1.6", "1.7", "0"], ["Poland", "2.2", "1.9", "1.6", "2.4", "2.5", "-0.5"], ["Spain", "4.8", "5", "2.3", "4.1", "5.5", "1.4"], ["Sweden", "0.7", "1", "0.6", "1.9", "0.8", "-0.2"], ["United Kingdom", "0.9", "1.4", "1.4", "1.9", "1.2", "-0.4"]], "labels": {"name": "Year-2017", "values": ["France", "Germany", "Netherlands", "Poland", "Spain", "Sweden", "United Kingdom"]}, "metadata": {"link": "https://www.ivir.nl/publicaties/download/Global-Online-Piracy-Study.pdf", "type": "Problem", "unit": null, "year": "2017", "title": "Consumption of Music Per Type of Illegal Channel (Per Capita, Internet Population)", "topic": "Copyright Infringement", "method": "Survey (N=18578)", "source": "Institute for Information Law of the University of Amsterdam. Global Online Piracy Study (Amsterdam: Institute for Information Law of the University of Amsterdam, 2018)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "63", "geographical": "European Union"}, "description": "This table provides details about the number of albums and hours of listening via illegal means that respondents reported per capita (for the internet-using population) among several countries. Respondents from Spain reported illegally downloading significantly more albums than respondents from other countries."},
{"data": [{"data": [5, 8, 7, 4, 5, 10, 6], "name": "Other"}, {"data": [5, 7, 11, 5, 7, 9, 6], "name": "The ease to add music to my collection"}, {"data": [1, 3, 2, 1, 1, 3, 4], "name": "It is unlikely to get caught"}, {"data": [5, 6, 3, 3, 5, 2, 6], "name": "The speed or reliability"}, {"data": [12, 11, 13, 19, 20, 4, 15], "name": "The ease of use"}, {"data": [6, 7, 10, 12, 6, 25, 8], "name": "Non-availability through legal channels"}, {"data": [14, 12, 7, 15, 9, 9, 18], "name": "The quality of sound (bitrate)"}, {"data": [6, 6, 9, 8, 9, 5, 4], "name": "It was the first site I found"}, {"data": [47, 39, 38, 33, 40, 33, 33], "name": "The price"}], "_data": [["Countries", "Other", "The ease to add music to my collection", "It is unlikely to get caught", "The speed or reliability", "The ease of use", "Non-availability through legal channels", "The quality of sound (bitrate)", "It was the first site I found", "The price"], ["France", "5", "5", "1", "5", "12", "6", "14", "6", "47"], ["Germany", "8", "7", "3", "6", "11", "7", "12", "6", "39"], ["Netherlands", "7", "11", "2", "3", "13", "10", "7", "9", "38"], ["Poland", "4", "5", "1", "3", "19", "12", "15", "8", "33"], ["Spain", "5", "7", "1", "5", "20", "6", "9", "9", "40"], ["Sweden", "10", "9", "3", "2", "4", "25", "9", "5", "33"], ["United Kindgom", "6", "6", "4", "6", "15", "8", "18", "4", "33"]], "labels": {"name": "Countries", "values": ["France", "Germany", "Netherlands", "Poland", "Spain", "Sweden", "United Kindgom"]}, "metadata": {"link": "https://www.ivir.nl/publicaties/download/Global-Online-Piracy-Study.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2017", "title": "Primary Reason for Downloading Music From Illegal Sources", "topic": "Copyright Infringement", "method": "Survey (N=18578)", "source": "Institute for Information Law of the University of Amsterdam. Global Online Piracy Study (Amsterdam: Institute for Information Law of the University of Amsterdam, 2018)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "64", "geographical": "European Union"}, "description": "The chart illustrates the distribution of the respondents' primary reasons for downloading music from illegal sources. The most common answer by far was the price. "},
{"data": [{"data": [17, 24, 21, 8, 11, 29, 9], "name": "Other"}, {"data": [2, 1, 1, 2, 3, 2, 1], "name": "It is unlikely to get caught"}, {"data": [4, 9, 3, 6, 4, 2, 6], "name": "The speed or reliability"}, {"data": [17, 10, 21, 16, 16, 7, 16], "name": "The ease of use"}, {"data": [9, 10, 10, 14, 11, 13, 10], "name": "Non-availability through legal channels"}, {"data": [9, 10, 7, 13, 14, 11, 11], "name": "The quality of sound (bitrate)"}, {"data": [10, 7, 9, 8, 10, 9, 10], "name": "It was the first site I found"}, {"data": [33, 30, 28, 34, 31, 27, 37], "name": "The price"}], "_data": [["Countries", "Other", "It is unlikely to get caught", "The speed or reliability", "The ease of use", "Non-availability through legal channels", "The quality of sound (bitrate)", "It was the first site I found", "The price"], ["France", "17", "2", "4", "17", "9", "9", "10", "33"], ["Germany", "24", "1", "9", "10", "10", "10", "7", "30"], ["Netherlands", "21", "1", "3", "21", "10", "7", "9", "28"], ["Poland", "8", "2", "6", "16", "14", "13", "8", "34"], ["Spain", "11", "3", "4", "16", "11", "14", "10", "31"], ["Sweden", "29", "2", "2", "7", "13", "11", "9", "27"], ["United Kingdom ", "9", "1", "6", "16", "10", "11", "10", "37"]], "labels": {"name": "Countries", "values": ["France", "Germany", "Netherlands", "Poland", "Spain", "Sweden", "United Kingdom "]}, "metadata": {"link": "https://www.ivir.nl/publicaties/download/Global-Online-Piracy-Study.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2017", "title": "Primary Reason for Streaming Music From Illegal Sources", "topic": "Copyright Infringement", "method": "Survey (N=18578)", "source": "Institute for Information Law of the University of Amsterdam. Global Online Piracy Study (Amsterdam: Institute for Information Law of the University of Amsterdam, 2018)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "64.1", "geographical": "European Union"}, "description": "The chart illustrates the distribution of the respondents' primary reasons for streaming music from illegal sources. The most common answer by far was the price."},
{"data": [{"data": [-0.43, -0.43, -0.36, -0.39, -0.43, -0.43, -0.49], "name": "Estimated displacement of first legal views"}], "_data": [["Estimated displacement", "Estimated displacement of first legal views"], ["France", "-0.43"], ["Germany", "-0.43"], ["Netherlands", "-0.36"], ["Poland", "-0.39"], ["Spain", "-0.43"], ["Sweden", "-0.43"], ["United Kingdom", "-0.49"]], "labels": {"name": "Estimated displacement", "values": ["France", "Germany", "Netherlands", "Poland", "Spain", "Sweden", "United Kingdom"]}, "metadata": {"link": "https://www.ivir.nl/publicaties/download/Global-Online-Piracy-Study.pdf", "type": "Problem", "unit": "Value", "year": "2017", "title": "Estimated Displacement of First Legal Views by First Illegal View per Country", "topic": "Copyright Infringement", "method": "Survey (N=18578)", "source": "Institute for Information Law of the University of Amsterdam. Global Online Piracy Study (Amsterdam: Institute for Information Law of the University of Amsterdam, 2018)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "65", "geographical": "European Union"}, "description": "The chart shows the estimated displacement of first legal views by first illegal views per country. Based on this graph, illegal views displace legal ones the most in the United Kingdom among the countries studied, and least in the Netherlands."},
{"data": [{"data": [90.8, 32.1, 13.9, 25], "name": "General user (%)"}, {"data": [96.8, 88.2, 40.5, 95.5], "name": "Trusted flagger/reporter (%)"}], "_data": [["Platform", "General user (%)", "Trusted flagger/reporter (%)"], ["Facebook", "90.8", "96.8"], ["Twitter", "32.1", "88.2"], ["YouTube", "13.9", "40.5"], ["Instagram", "25", "95.5"]], "labels": {"name": "Platform", "values": ["Facebook", "Twitter", "YouTube", "Instagram"]}, "metadata": {"link": "https://ec.europa.eu/info/sites/info/files/code_of_conduct_factsheet_7_web.pdf", "type": "Solution", "unit": "Share of users receiving feedback (%)", "year": "2019", "Range": "0 100", "title": "Feedback Provided to Different Types of User by Social Media Platforms ", "topic": "Hate Speech", "method": "Self-reporting", "source": "European Commission. Fourth Evaluation on the Code of Conduct on Countering Illegal Hate Speech Online (Brussels: European Commission, 2019)  ", "sub_topic": "Removal of hate speech", "chart_number": "66", "geographical": "European Union"}, "description": "The chart shows the percent of users who reported posts who received feedback regarding their report on various social media platforms. Facebook was most likely to provide feedback to normal users and to trusted flaggers. All of the platforms were more likely to provide feedback to trusted flaggers than to normal users."},
{"data": [{"data": [2378, 2082, 1397, 1384, 1342, 1250, 1151, 1003, 875, 462, 399, 386, 362, 277, 271, 236, 202, 181, 148, 147, 135, 128, 118, 103, 89, 84, 72, 28], "name": "Google Ads accounts with misrepresentation violations "}], "_data": [["Country", "Google Ads accounts with misrepresentation violations "], ["Germany", "2378"], ["United Kingdom", "2082"], ["Poland", "1397"], ["France", "1384"], ["Spain", "1342"], ["Slovakia", "1250"], ["Italy", "1151"], ["Denmark", "1003"], ["Netherlands", "875"], ["Romania", "462"], ["Portugal", "399"], ["Slovenia", "386"], ["Czech Republic", "362"], ["Bulgaria", "277"], ["Hungary", "271"], ["Belgium", "236"], ["Greece", "202"], ["Croatia", "181"], ["Ireland", "148"], ["Austria", "147"], ["Lithuania", "135"], ["Cyprus", "128"], ["Sweden", "118"], ["Malta", "103"], ["Finland", "89"], ["Estonia", "84"], ["Latvia", "72"], ["Luxembourg", "28"]], "labels": {"name": "Country", "values": ["Germany", "United Kingdom", "Poland", "France", "Spain", "Slovakia", "Italy", "Denmark", "Netherlands", "Romania", "Portugal", "Slovenia", "Czech Republic", "Bulgaria", "Hungary", "Belgium", "Greece", "Croatia", "Ireland", "Austria", "Lithuania", "Cyprus", "Sweden", "Malta", "Finland", "Estonia", "Latvia", "Luxembourg"]}, "metadata": {"link": "https://ec.europa.eu/digital-single-market/en/news/last-intermediate-results-eu-code-practice-against-disinformation", "type": "Solution", "unit": "Number of Google accounts", "year": "2019", "title": "Google Ads Accounts With Misrepresentation Violations (by Country)", "topic": "Disinformation", "method": "Self-reporting", "source": "Google. Code of Practice on Disinformation: May 2019 (google.com, 2019)", "sub_topic": "Removal of disinformation", "chart_number": "68", "geographical": "European Union"}, "description": "The chart shows the number of Google Ads accounts with adds violating Google's standards regarding misrepresentation, by billing country. The data used are those reported by Google under the European Union Code of Practice against Disinformation. The results show that Germany is the billing country for the highest number of these accounts. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [1462, 567, 522, 333, 315, 313, 300, 297, 278, 214, 211, 109, 102, 94, 67, 49, 41, 37, 36, 25, 22, 15, 13, 13, 10, 9, 6, 5], "name": "Google Ads Accounts "}], "_data": [["Country", "Google Ads Accounts "], ["United Kingdom", "1462"], ["Germany", "567"], ["Poland", "522"], ["Spain", "333"], ["France", "315"], ["Romania", "313"], ["Denmark", "300"], ["Croatia", "297"], ["Italy", "278"], ["Netherlands", "214"], ["Sweden", "211"], ["Bulgaria", "109"], ["Ireland", "102"], ["Cyprus", "94"], ["Czech Republic", "67"], ["Portugal", "49"], ["Greece", "41"], ["Hungary", "37"], ["Austria", "36"], ["Belgium", "25"], ["Letonia", "22"], ["Malta", "15"], ["Finland", "13"], ["Slovakia", "13"], ["Estonia", "10"], ["Latvia", "9"], ["Slovenia", "6"], ["Luxembourg", "5"]], "labels": {"name": "Country", "values": ["United Kingdom", "Germany", "Poland", "Spain", "France", "Romania", "Denmark", "Croatia", "Italy", "Netherlands", "Sweden", "Bulgaria", "Ireland", "Cyprus", "Czech Republic", "Portugal", "Greece", "Hungary", "Austria", "Belgium", "Letonia", "Malta", "Finland", "Slovakia", "Estonia", "Latvia", "Slovenia", "Luxembourg"]}, "metadata": {"link": "https://ec.europa.eu/digital-single-market/en/news/last-intermediate-results-eu-code-practice-against-disinformation", "type": "Solution", "unit": "Number of Google accounts", "year": "2019", "title": "Google Ads Accounts With Violation of the Policy \"Insufficient Original Content\" (by Country)", "topic": "Disinformation", "method": "Self-reporting", "source": "Google. Code of Practice on Disinformation: May 2019 (google.com, 2019)", "sub_topic": "Removal of disinformation", "chart_number": "69", "geographical": "European Union"}, "description": "This data reported by Google under the European Union Code of Practice against Disinformation shows the number of Google Ads accounts whose adds violated Google's standards regarding original content, split up by billing country. The United Kingdom was listed as the billing country for the highest number of these accounts."},
{"data": [{"data": [78, 70, 67, 39, 24, 23, 17, 17, 13, 13, 12, 12, 11, 9, 9, 9, 9, 9, 8, 8, 8, 7, 7, 7, 6, 6, 5], "name": "Number of Ads"}], "_data": [[" Country", "Number of Ads"], ["Ireland", "78"], ["United Kingdom", "70"], ["Germany", "67"], ["France", "39"], ["Spain", "24"], ["Netherlands", "23"], ["Austria", "17"], ["Czech Republic", "17"], ["Italy", "13"], ["Poland", "13"], ["Belgium", "12"], ["Portugal", "12"], ["Sweden", "11"], ["Denmark", "9"], ["Finland", "9"], ["Greece", "9"], ["Romania", "9"], ["Slovenia", "9"], ["Hungary", "8"], ["Luxembourg", "8"], ["Malta", "8"], ["Bulgaria", "7"], ["Croatia", "7"], ["Slovakia", "7"], ["Estonia", "6"], ["Latvia", "6"], ["Lithuania", "5"]], "labels": {"name": " Country", "values": ["Ireland", "United Kingdom", "Germany", "France", "Spain", "Netherlands", "Austria", "Czech Republic", "Italy", "Poland", "Belgium", "Portugal", "Sweden", "Denmark", "Finland", "Greece", "Romania", "Slovenia", "Hungary", "Luxembourg", "Malta", "Bulgaria", "Croatia", "Slovakia", "Estonia", "Latvia", "Lithuania"]}, "metadata": {"link": "https://ec.europa.eu/digital-single-market/en/news/last-intermediate-results-eu-code-practice-against-disinformation", "type": "Problem", "unit": "Number of Ads", "year": "2019", "title": "Number of Ads Blocked by Twitter (May 2019)", "topic": "Disinformation", "method": "Self-reporting", "source": "Twitter. Code of Practice on Disinformation: May Report (twitter.com, 2019)", "sub_topic": "Removal of disinformation", "chart_number": "71", "geographical": "European Union"}, "description": "The chart, based on data reported by Twitter under the European Union Code of Practice against Disinformation, shows the distribution of ads from non-certified accounts prevented from targeting European Union member states between April and May 2019. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020. Ireland was targeted by the most ads of this kind, while Lithuania by the least ones."},
{"data": [{"data": [233, 130, 125, 116, 91, 75, 68, 66, 44, 41, 41, 39, 38, 35, 34, 29, 27, 27, 23, 23, 22, 19, 19, 18, 16, 16, 13], "name": "Number of Ads"}], "_data": [["Targeted Country", "Number of Ads"], ["United Kingdom", "233"], ["Sweden", "130"], ["Spain", "125"], ["France", "116"], ["Germany", "91"], ["Italy", "75"], ["Ireland", "68"], ["Netherlands", "66"], ["Estonia", "44"], ["Belgium", "41"], ["Finland", "41"], ["Austria", "39"], ["Poland", "38"], ["Denmark", "35"], ["Portugal", "34"], ["Bulgaria", "29"], ["Greece", "27"], ["Romania", "27"], ["Czech Republic", "23"], ["Hungary", "23"], ["Slovenia", "22"], ["Malta", "19"], ["Slovakia", "19"], ["Croatia", "18"], ["Latvia", "16"], ["Luxembourg", "16"], ["Lithuania", "13"]], "labels": {"name": "Targeted Country", "values": ["United Kingdom", "Sweden", "Spain", "France", "Germany", "Italy", "Ireland", "Netherlands", "Estonia", "Belgium", "Finland", "Austria", "Poland", "Denmark", "Portugal", "Bulgaria", "Greece", "Romania", "Czech Republic", "Hungary", "Slovenia", "Malta", "Slovakia", "Croatia", "Latvia", "Luxembourg", "Lithuania"]}, "metadata": {"link": "https://ec.europa.eu/digital-single-market/en/news/last-intermediate-results-eu-code-practice-against-disinformation", "type": "Problem", "unit": "Number of Ads", "year": "2019", "title": "Number of Ads Rejected Under Twitter's Unacceptable Business Practices Ads Policy", "topic": "Disinformation", "method": "Self-reporting", "source": "Twitter. Code of Practice on Disinformation: May Report (twitter.com, 2019)", "sub_topic": "Removal of disinformation", "chart_number": "72", "geographical": "European Union"}, "description": "Based on the data reported by Twitter under the European Union Code of Practice against Disinformation, in the period 01 to 20 May 2019, the United Kingdom was the target of the most rejected ads under Twitter's Unacceptable Business Practices Ads Policy. It was followed by Sweden and Spain. Lithuania is the country targeted by the fewest rejected ads."},
{"data": [{"data": [211, 201, 149, 107, 106, 82, 81, 79, 76, 63, 61, 61, 59, 54, 52, 50, 49, 48, 48, 47, 45, 43, 42, 42, 41, 41, 37], "name": "Number of Ads"}], "_data": [["Targeted Country", "Number of Ads"], ["United Kingdom", "211"], ["Sweden", "201"], ["France", "149"], ["Spain", "107"], ["Germany", "106"], ["Netherlands", "82"], ["Italy", "81"], ["Ireland", "79"], ["Belgium", "76"], ["Denmark", "63"], ["Austria", "61"], ["Poland", "61"], ["Portugal", "59"], ["Finland", "54"], ["Czech Republic", "52"], ["Hungary", "50"], ["Croatia", "49"], ["Greece", "48"], ["Romania", "48"], ["Malta", "47"], ["Luxembourg", "45"], ["Slovenia", "43"], ["Bulgaria", "42"], ["Slovakia", "42"], ["Estonia", "41"], ["Latvia", "41"], ["Lithuania", "37"]], "labels": {"name": "Targeted Country", "values": ["United Kingdom", "Sweden", "France", "Spain", "Germany", "Netherlands", "Italy", "Ireland", "Belgium", "Denmark", "Austria", "Poland", "Portugal", "Finland", "Czech Republic", "Hungary", "Croatia", "Greece", "Romania", "Malta", "Luxembourg", "Slovenia", "Bulgaria", "Slovakia", "Estonia", "Latvia", "Lithuania"]}, "metadata": {"link": "https://ec.europa.eu/digital-single-market/en/news/last-intermediate-results-eu-code-practice-against-disinformation", "type": "Problem", "unit": "Number of Ads", "year": "2019", "title": "Number of Ads Rejected Under Twitter's Quality Ads Policy", "topic": "Disinformation", "method": "Self-reporting", "source": "Twitter. Code of Practice on Disinformation: May Report (twitter.com, 2019)", "sub_topic": "Removal of disinformation", "chart_number": "73", "geographical": "European Union"}, "description": "Based on the data reported by Twitter under the European Union Code of Practice against Disinformation, in the period 01 to 20 May 2019, the United Kingdom was the target of the most rejected ads under Twitter's Quality Ads Policy. It was followed by Sweden and France. Lithuania is the country targeted by the fewest rejected ads. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [0.05, 0.12, 0.26, 0.75], "name": "Average number of fake news shares"}], "_data": [["Age group", "Average number of fake news shares"], ["18-29", "0.05"], ["30-44", "0.12"], ["45-65", "0.26"], ["Over 65", "0.75"]], "labels": {"name": "Age group", "values": ["18-29", "30-44", "45-65", "Over 65"]}, "metadata": {"link": "https://advances.sciencemag.org/content/advances/5/1/eaau4586.full.pdf", "type": "Problem", "unit": "Average number of fake news shares", "year": "2019", "title": "Average Number of Fake News Stories Shared on Facebook, by Age Group", "topic": "Disinformation", "method": "Survey (N=5000)", "source": "Guess, Andrew, Jonathan Nagler, Joshua Tucker. Less Than You Think: Prevalence and Predictions of Fake News Dissemination on Facebook (New York: American Association for the Advancement of Science, 2019)", "sub_topic": "Prevalence of disinformation", "chart_number": "78", "geographical": "United States"}, "description": "The chart shows that Americans over 65 were more likely to share fake news to their Facebook friends, regardless of their education, ideology, and partisanship. The oldest age group was likely to share nearly seven times as many articles from fake news domains on Facebook as those in the youngest age group, or about 2.3 times as many as those in the next-oldest age group. The data regarding the age group 18-29 and 30-44 are not displayed in the source, therefore the value of data in this chart are approximate, determined with pixel count."},
{"data": [{"data": [83, 85, 81, 84, 94, 91, 92, 91], "name": "Had at least one nuisance"}, {"data": [41, 36, 34, 60, 43, 51, 50, 54], "name": "Had at least one harmful"}], "_data": [["%", "Had at least one nuisance", "Had at least one harmful"], ["Reminder of total", "83", "41"], ["Non-consumers of cultural products", "85", "36"], ["Always legal", "81", "34"], ["Formerly infringing", "84", "60"], ["Occasionally infringing", "94", "43"], ["Regular infringing", "91", "51"], ["Do legal streaming", "92", "50"], ["Do illegal downloading", "91", "54"]], "labels": {"name": "%", "values": ["Reminder of total", "Non-consumers of cultural products", "Always legal", "Formerly infringing", "Occasionally infringing", "Regular infringing", "Do legal streaming", "Do illegal downloading"]}, "metadata": {"link": "https://www.hadopi.fr/sites/default/files/sites/default/files/ckeditor_files/Activity-report-2016-17-HADOPI.pdf", "type": "Problem", "year": "2017", "title": "Total Copyright-Infringing Content ", "topic": "Copyright Infringement", "method": "Survey", "source": "French High Authority for the Dissemination of Works and the Protection of Rights on the Internet. Activity Report (Paris: HADOPI, 2017)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "75", "geographical": "France"}, "description": "This graph shows the percent of people who report having had at least one nuisance or at least one harmful incident related to copyright infringement online in France. It is based on data gathered through surveys by Haute Autorité Française pour la Diffusion des Oeuvres et la Protection des droits sur Internet (HADOPI ), and shows that significant fractions of respondents experienced nuisances or harms related to online copyright infringement."},
{"data": [{"data": [90, 10], "name": "Percent of titles posted"}], "_data": [["Poster", "Percent of titles posted"], ["14 Contributor accounts", "90%"], ["Other accounts", "10%"]], "labels": {"name": "Poster", "values": ["14 Contributor accounts", "Other accounts"]}, "metadata": {"link": "https://www.hadopi.fr/sites/default/files/sites/default/files/ckeditor_files/Activity-report-2016-17-HADOPI.pdf", "type": "Problem", "year": "2017", "title": "Breakdown of Titles Posted by Contributor Account On a Pirate Site", "topic": "Copyright Infringement", "method": "Administrative data", "source": "French High Authority for the Dissemination of Works and the Protection of Rights on the Internet. Activity Report (Paris: HADOPI, 2017)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "76", "geographical": "France"}, "description": "This chart illustrates the unequal contributions of accounts to a piracy website studied by HADOPI (Haute Autorité Française pour la Diffusion des Oeuvres et la Protection des droits sur Internet). Only 14 contributors accounted for 90% of the content uploaded, showing that much of the illegal activity on the site was concentrated in a specific group of users."},
{"data": [{"data": [20598, 82256, 83299, 147570, 148944, 152665, 178286], "name": "Second notices sent since 2010"}], "_data": [["Time Period", "Second notices sent since 2010"], ["Sep 10 - Jun 11", "20598"], ["Jul 11 - Jun 12", "82256"], ["Jul 12 - Jun 13", "83299"], ["Jul 13 - Jun 14", "147570"], ["Jul 14 - Jun 15", "148944"], ["Jul 15 - Jun 16", "152665"], ["Jul 16 - Jun 17", "178286"]], "labels": {"name": "Time Period", "values": ["Sep 10 - Jun 11", "Jul 11 - Jun 12", "Jul 12 - Jun 13", "Jul 13 - Jun 14", "Jul 14 - Jun 15", "Jul 15 - Jun 16", "Jul 16 - Jun 17"]}, "metadata": {"link": "https://www.hadopi.fr/sites/default/files/sites/default/files/ckeditor_files/Activity-report-2016-17-HADOPI.pdf", "type": "Problem", "year": "2017", "title": "Second Notices Sent Since 2010", "topic": "Copyright Infringement", "method": "Administrative data", "source": "French High Authority for the Dissemination of Works and the Protection of Rights on the Internet. Activity Report (Paris: HADOPI, 2017)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "77.1", "geographical": "France"}, "description": "This column chart shows the cumulative number of second notices sent by Haute Autorité Française pour la Diffusion des Oeuvres et la Protection des droits sur Internet (HADOPI) from 2010 to 2017. HADOPI sent the most notices between July 2011 and June 2012 and between July 2013 and June 2014.  The second notice refers to a graduated two-step response implemented by HADOPI to\nremind internet subscribers of their responsibility regarding their Internet connection which should not be used to make works protected by copyright (or related rights) available for piracy."},
{"data": [{"data": [0.05, 0.12, 0.26, 0.75], "name": "Mean Number of Fake News Stories Shared"}], "_data": [["Age group", "Mean Number of Fake News Stories Shared"], ["18-29", "0.05"], ["30-44", "0.12"], ["45-65", "0.26"], ["Over 65", "0.75"]], "labels": {"name": "Age group", "values": ["18-29", "30-44", "45-65", "Over 65"]}, "metadata": {"link": "https://advances.sciencemag.org/content/advances/5/1/eaau4586.full.pdf", "type": "Problem", "unit": "Mean number of fake news stories shared", "year": "2019", "title": "Average Number of Fake News Stories Shared on Facebook, by Age Group", "topic": "Disinformation", "method": "Survey (N=5000)", "source": "Guess, Andrew, Jonathan Nagler, Joshua Tucker. Less Than You Think: Prevalence and Predictions of Fake News Dissemination on Facebook (New York: American Association for the Advancement of Science, 2019)", "sub_topic": "Prevalence of disinformation", "chart_number": "78", "geographical": "United States"}, "description": "The chart shows that that the oldest Americans, especially those over 65, were more likely to share fake news to their Facebook friends. This is true even when holding other characteristics—including education, ideology, and partisanship—constant.  The coefficient on “Age over 65” implies that being in the oldest age group was associated with sharing nearly seven times as many articles from fake news domains on Facebook as those in the youngest age group, or about 2.3 times as many as those in the next-oldest age group, holding the effect of ideology, education, and the total number of web links shared constant."},
{"data": [{"data": [470935, 682525, 759387, 1336634, 1648402, 2083082, 1831856], "name": "First notices sent since 2010"}], "_data": [["Time period", "First notices sent since 2010"], ["Sep 10 - Jun 11", "470935"], ["Jul 11 - Jun 12", "682525"], ["Jul 12 - Jun 13", "759387"], ["Jul 13 - Jun 14", "1336634"], ["Jul 14 - Jun 15", "1648402"], ["Jul 15 - Jun 16", "2083082"], ["Jul 16 - Jun 17", "1831856"]], "labels": {"name": "Time period", "values": ["Sep 10 - Jun 11", "Jul 11 - Jun 12", "Jul 12 - Jun 13", "Jul 13 - Jun 14", "Jul 14 - Jun 15", "Jul 15 - Jun 16", "Jul 16 - Jun 17"]}, "metadata": {"link": "https://www.hadopi.fr/sites/default/files/sites/default/files/ckeditor_files/Activity-report-2016-17-HADOPI.pdf", "type": "Problem", "unit": "Number of first notices sent", "year": "2010-2017", "title": "Number of First Notices Sent by French High Authority for the Dissemination of Works and the Protection of Rights on the Internet (HADOPI)", "topic": "Copyright Infringement", "method": "Administrative data", "source": "French High Authority for the Dissemination of Works and the Protection of Rights on the Internet. Activity Report (Paris: HADOPI, 2017)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "79", "geographical": "France"}, "description": "The chart shows the number of first notices sent by Haute Autorité Française pour la Diffusion des Oeuvres et la Protection des Droits sur Internet since 2010 until 2017 to the holder of an Internet subscription whose access has been used to commit acts of copyright infringement. "},
{"data": [{"data": [100, 135, 240, 388, 546], "name": "Total number of professional monitoring cases"}], "_data": [["Number of professional monitoring cases", "Total number of professional monitoring cases"], ["January 2013", "100"], ["January 2014", "135"], ["January 2015", "240"], ["January 2016", "388"], ["January 2017", "546"]], "labels": {"name": "Number of professional monitoring cases", "values": ["January 2013", "January 2014", "January 2015", "January 2016", "January 2017"]}, "metadata": {"link": "https://www.hadopi.fr/sites/default/files/sites/default/files/ckeditor_files/Activity-report-2016-17-HADOPI.pdf", "type": "Problem", "year": "2017", "title": "Evolution of the Total Number of Professional Monitoring Cases of Copyright Infringement (2013- 2017)", "topic": "Copyright Infringement", "method": "Administrative data", "source": "French High Authority for the Dissemination of Works and the Protection of Rights on the Internet. Activity Report (Paris:HADOPI, 2017)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "80", "geographical": "France"}, "description": "This graph shows how the number of professional monitoring cases has changed between 2013 and 2017, based on data from HADOPI (Haute Autorité Française pour la Diffusion des Oeuvres et la Protection des Droits sur Internet). The number of cases has increased consistently between 2013 and 2017."},
{"data": [{"data": [20, 96], "name": "No "}, {"data": [80, 4], "name": "Yes"}], "_data": [["Year", "No ", "Yes"], ["2009", "20", "80"], ["2014", "96", "4"]], "labels": {"name": "Year", "values": ["2009", "2014"]}, "metadata": {"link": "https://kreatywna-europa.eu/wp-content/uploads/2018/11/IRIS-plus-2015en3.pdf", "type": "Problem", "unit": "Percent (%)", "year": "2015", "title": "Percentage of People Reporting Having Downloaded or Accessed Copyright-Protected Content Illegally Over The Last 12 Months (2014)", "topic": "Copyright Infringement", "method": "Survey", "source": "Cabrera Blázquez, Francisco Javier, Maja Cappello, Christian Grece and Sophie Valais. Copyright Enforcement Online: Policies and Mechanisms (Strasbourg: European Audiovisual Observatory, 2015)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "81", "geographical": "Norway"}, "description": "The chart shows that the number of Norwegians under 30 reporting that they illegally downloaded or accessed copyright-protected content over the last 12 months decreased dramatically between 2009 and 2014."},
{"data": [{"data": [26, 17, 9, 5, 3], "name": "Percent"}], "_data": [["Age groups", "Percent"], ["15 to 24 years", "26"], ["25 to 34 years", "17"], ["35 to 44 years", "9"], ["45 to 54 years", "5"], ["over 55 years", "3"]], "labels": {"name": "Age groups", "values": ["15 to 24 years", "25 to 34 years", "35 to 44 years", "45 to 54 years", "over 55 years"]}, "metadata": {"link": "https://kreatywna-europa.eu/wp-content/uploads/2018/11/IRIS-plus-2015en3.pdf", "type": "Problem", "unit": "%", "year": "2013", "title": "Percentage of People Reporting Having Downloaded or Accessed Copyright-Protected Content Illegally, by Age Groups (2013)", "topic": "Copyright Infringement", "method": "Survey (N=26549)", "source": "Cabrera Blázquez, Francisco Javier, Maja Cappello, Christian Grece and Sophie Valais. Copyright Enforcement Online: Policies and Mechanisms (Strasbourg: European Audiovisual Observatory, 2015)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "82", "geographical": "European Union"}, "description": "This column chart shows that the percent of respondents who report that they have illlegally downloaded or accessed copyright-protected content over the last 12 months is highest among respondents 15 to 24 years old and consistently decreases as respondents' ages increase after that. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [31, 13], "name": "Men"}, {"data": [21, 6], "name": "Women"}], "_data": [["Age groups", "Men", "Women"], ["15 to 24 years", "31", "21"], ["All", "13", "6"]], "labels": {"name": "Age groups", "values": ["15 to 24 years", "All"]}, "metadata": {"link": "https://kreatywna-europa.eu/wp-content/uploads/2018/11/IRIS-plus-2015en3.pdf", "type": "Problem", "unit": "Percent (%)", "year": "2013", "title": "Percentage of People Reporting Having Downloaded or Accessed Copyright-Protected Content Illegally, by Gender (2013)", "topic": "Copyright Infringement", "method": "Survey (N=26549)", "source": "Cabrera Blázquez, Francisco Javier, Maja Cappello, Christian Grece and Sophie Valais. Copyright Enforcement Online: Policies and Mechanisms (Strasbourg: European Audiovisual Observatory, 2015)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "83", "geographical": "European Union"}, "description": "This segmented column chart shows that respondents aged 15 to 24 years old are more likely than the general population to report having illegally accessed or downloaded copyright-protected content in the last 12 months, and that men are more likely than women to report having done so. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [3, 6, 10, 27], "name": "Percent"}], "_data": [["Education groups", "Percent"], ["Finished their studies before the age of 15", "3"], ["Finished their studies between the ages of 16 and 19 years old", "6"], ["Finished their studies after 20 years old", "10"], ["Citizens still studying", "27"]], "labels": {"name": "Education groups", "values": ["Finished their studies before the age of 15", "Finished their studies between the ages of 16 and 19 years old", "Finished their studies after 20 years old", "Citizens still studying"]}, "metadata": {"link": "https://kreatywna-europa.eu/wp-content/uploads/2018/11/IRIS-plus-2015en3.pdf", "type": "Problem", "unit": "Percent (%)", "year": "2013", "title": "Percentage of People Reporting Having Downloaded or Accessed Copyright-Protected Content Illegally, by Education Level (2013)", "topic": "Copyright Infringement", "method": "Survey (N=26549)", "source": "Cabrera Blázquez, Francisco Javier, Maja Cappello, Christian Grece and Sophie Valais. Copyright Enforcement Online: Policies and Mechanisms (Strasbourg: European Audiovisual Observatory, 2015)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "84", "geographical": "European Union"}, "description": "This column chart shows that the percent of respondents who report having downloaded or accessed copyright-protected content in the last 12 months increases with education level and is highest for those currently studying. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [13, 4], "name": "Percentage"}], "_data": [["Countries", "Percentage"], ["Member states which joined the EU after 2004", "13"], ["15 other member states", "4"]], "labels": {"name": "Countries", "values": ["Member states which joined the EU after 2004", "15 other member states"]}, "metadata": {"link": "https://kreatywna-europa.eu/wp-content/uploads/2018/11/IRIS-plus-2015en3.pdf", "type": "Problem", "unit": "Percent (%)", "year": "2013", "title": "Percentage of People Reporting Having Downloaded or Accessed Copyright-Protected Content Illegally, Groups of European Union Member States (2013)", "topic": "Copyright Infringement", "method": "Survey (N=26549)", "source": "Cabrera Blázquez, Francisco Javier, Maja Cappello, Christian Grece and Sophie Valais. Copyright Enforcement Online: Policies and Mechanisms (Strasbourg: European Audiovisual Observatory, 2015)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "85", "geographical": "European Union"}, "description": "This table shows that the percent of respondents who report having downloaded or accessed copyright-protected content in the last 12 months is higher among respondents from member states which joined the European Union after 2004 than it is among other member states. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [65, 64, 62, 61, 60, 60, 59, 59, 58, 57, 56, 56, 56, 54, 54, 54, 54, 53, 53, 52, 52, 51, 50, 50, 49, 49, 47, 46, 42], "name": "Total \"At least once\""}, {"data": [34, 35, 37, 39, 39, 39, 40, 41, 42, 43, 44, 43, 44, 45, 45, 46, 42, 47, 47, 46, 47, 48, 50, 49, 49, 50, 52, 53, 56], "name": "Never"}, {"data": [1, 1, 1, null, 1, 1, 1, null, null, null, null, 1, null, 1, 1, null, 4, null, null, 2, 1, 1, null, 1, 2, 1, 1, 1, 2], "name": "Don't know"}], "_data": [["Country", "Total \"At least once\"", "Never", "Don't know"], ["Bulgaria", "65", "34", "1"], ["Latvia", "64", "35", "1"], ["Czech Republic", "62", "37", "1"], ["Slovakia", "61", "39", "0"], ["Poland", "60", "39", "1"], ["Estonia", "60", "39", "1"], ["Danemark", "59", "40", "1"], ["Netherlands", "59", "41", "0"], ["Finland", "58", "42", "0"], ["Spain", "57", "43", "0"], ["Italy", "56", "44", "0"], ["Luxembourg", "56", "43", "1"], ["Slovenia", "56", "44", "0"], ["Ireland", "54", "45", "1"], ["Hungary", "54", "45", "1"], ["Belgium", "54", "46", "0"], ["Lithuania", "54", "42", "4"], ["Greece", "53", "47", "0"], ["Malta", "53", "47", "0"], ["United Kingdom", "52", "46", "2"], ["European Union", "52", "47", "1"], ["Croatia", "51", "48", "1"], ["Portugal", "50", "50", "0"], ["Austria", "50", "49", "1"], ["Romania", "49", "49", "2"], ["Cyprus", "49", "50", "1"], ["Sweden", "47", "52", "1"], ["France", "46", "53", "1"], ["Germany", "42", "56", "2"]], "labels": {"name": "Country", "values": ["Bulgaria", "Latvia", "Czech Republic", "Slovakia", "Poland", "Estonia", "Danemark", "Netherlands", "Finland", "Spain", "Italy", "Luxembourg", "Slovenia", "Ireland", "Hungary", "Belgium", "Lithuania", "Greece", "Malta", "United Kingdom", "European Union", "Croatia", "Portugal", "Austria", "Romania", "Cyprus", "Sweden", "France", "Germany"]}, "metadata": {"link": "https://ec.europa.eu/digital-single-market/en/news/flash-eurobarometer-illegal-content", "type": "Problem", "unit": "Per cent (%)", "year": "2018", "title": "Frequency of Using File sharing Services to Upload or Download Documents, Videos, Images or Music (by Country)", "topic": "Copyright Infringement", "method": "Survey (N=33244)", "source": "European Commission. Flash Eurobarometer 469 Report: Illegal Content Online (Brussels: European Commission, 2018)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "86", "geographical": "European Union"}, "description": "The graph shows the frequence of respondents in using file sharing services to upload or download content. Respondents in Bulgaria were the most likely to report having use these services at least once, while respondents in Germant were least likely to do so. The respondents were asked the following question: \"How often do you do the following? Use file sharing services to upload or download documents, videos, images or music\". European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [69, 49, 15, 7], "name": "Totally agree"}, {"data": [21, 36, 29, 24], "name": "Tend to agree"}, {"data": [4, 8, 26, 40], "name": "Tend to disagree"}, {"data": [4, 4, 13, 25], "name": "Totally disagree"}, {"data": [2, 3, 17, 4], "name": "Don't know"}], "_data": [["Statement", "Totally agree", "Tend to agree", "Tend to disagree", "Totally disagree", "Don't know"], ["Arrangements need to be in place to limit the spread of illegal content on the Internet", "69", "21", "4", "4", "2"], ["Freedom of expression needs to be protected online", "49", "36", "8", "4", "3"], ["Internet hosting services are effective in tackling illlegal content", "15", "29", "26", "13", "17"], ["The Internet is safe for its users", "7", "24", "40", "25", "4"]], "labels": {"name": "Statement", "values": ["Arrangements need to be in place to limit the spread of illegal content on the Internet", "Freedom of expression needs to be protected online", "Internet hosting services are effective in tackling illlegal content", "The Internet is safe for its users"]}, "metadata": {"link": "https://ec.europa.eu/digital-single-market/en/news/flash-eurobarometer-illegal-content", "type": "Problem", "unit": "Percent (%)", "year": "2018", "title": "Users’ Perception on the Internet’s Safety and on the Measures Taken Against Illegal Content", "topic": "Copyright Infringement", "method": "Survey (N=33244)", "source": "European Commission. Flash Eurobarometer 469 Report: Illegal Content Online (Brussels: European Commission, 2018)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "87", "geographical": "European Union"}, "description": "The graph shows the percent of respondents who agree with various statements about the internet, freedom of expression, and illegal content. The chart shows that there is strong support for arrangements to limit the spread of illegal content online but also strong agreement that freedom of expression needs to be protected online. Respondents were asked \"Do you agree or disagree each of the following?.\""},
{"data": [{"data": [59, 21, 9, 8, 10, 1], "name": "European Union"}], "_data": [["Label", "European Union"], ["You took no action", "59%"], ["You informed the internet service hosting the content", "21%"], ["You contacted directly the person or organisation who had uploaded the content", "9%"], ["You alerted the police or relevant authorities", "8%"], ["Other", "10%"], ["Don't know", "1%"]], "labels": {"name": "Label", "values": ["You took no action", "You informed the internet service hosting the content", "You contacted directly the person or organisation who had uploaded the content", "You alerted the police or relevant authorities", "Other", "Don't know"]}, "metadata": {"link": "https://ec.europa.eu/digital-single-market/en/news/flash-eurobarometer-illegal-content", "type": "Problem", "unit": "Percent (%)", "year": "2018", "title": "Action Taken After Encountering Illegal Content (2018)", "topic": "Illegal Content", "method": "Survey (N=18313)", "source": "European Commission. Flash Eurobarometer 469 Report: Illegal Content Online (Brussels: European Commission, 2018)", "sub_topic": "Prevalence of illegal content", "chart_number": "91", "geographical": "European Union"}, "description": "The chart shows that the majority of users took not action after encountering illegal content online. The chart results are based on the answers to the question “Q4. What action did you take after encountering this content?\", for which the selection of more than one answer is possible. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [59, 56, 77, 67, 65, 46, 68, 64, 72, 67, 47, 80, 56, 69, 73, 73, 54, 75, 73, 58, 58, 70, 62, 80, 81, 75, 71, 58, 53], "name": "You took no action"}, {"data": [21, 20, 8, 20, 17, 28, 9, 20, 17, 16, 28, 8, 15, 16, 15, 12, 25, 16, 11, 21, 22, 17, 20, 10, 10, 13, 19, 22, 25], "name": "You informed the internet service hosting the content"}, {"data": [9, 12, 6, 6, 7, 12, 9, 8, 3, 5, 9, 6, 13, 5, 7, 6, 13, 4, 8, 10, 12, 9, 8, 3, 4, 6, 6, 10, 10], "name": "You contacted directly the person or organisation who had uploaded the content"}, {"data": [8, 10, 3, 6, 5, 14, 4, 6, 5, 8, 6, 1, 10, 5, 4, 3, 12, 1, 4, 12, 10, 3, 7, 2, 3, 2, 6, 7, 9], "name": "You alerted the police or relevant authorities"}, {"data": [10, 9, 7, 5, 11, 10, 10, 9, 5, 7, 16, 5, 9, 8, 5, 7, 8, 5, 5, 9, 9, 6, 8, 6, 4, 7, 4, 7, 12], "name": "Other"}, {"data": [1, 2, 2, 2, 1, 2, 2, 2, null, 1, 3, 1, null, 1, null, null, 2, null, 3, 1, 2, null, 1, 1, null, 1, null, 3, 2], "name": "Don't know"}], "_data": [["Country", "You took no action", "You informed the internet service hosting the content", "You contacted directly the person or organisation who had uploaded the content", "You alerted the police or relevant authorities", "Other", "Don't know"], ["European Union", "59", "21", "9", "8", "10", "1"], ["Belgium", "56", "20", "12", "10", "9", "2"], ["Bulgaria", "77", "8", "6", "3", "7", "2"], ["Czech Republic", "67", "20", "6", "6", "5", "2"], ["Denmark", "65", "17", "7", "5", "11", "1"], ["Germany", "46", "28", "12", "14", "10", "2"], ["Estonia", "68", "9", "9", "4", "10", "2"], ["Ireland", "64", "20", "8", "6", "9", "2"], ["Greece", "72", "17", "3", "5", "5", "0"], ["Spain", "67", "16", "5", "8", "7", "1"], ["France", "47", "28", "9", "6", "16", "3"], ["Croatia", "80", "8", "6", "1", "5", "1"], ["Italy", "56", "15", "13", "10", "9", "0"], ["Cyprus", "69", "16", "5", "5", "8", "1"], ["Latvia", "73", "15", "7", "4", "5", "0"], ["Lithuania", "73", "12", "6", "3", "7", "0"], ["Luxembourg", "54", "25", "13", "12", "8", "2"], ["Hungary", "75", "16", "4", "1", "5", "0"], ["Malta", "73", "11", "8", "4", "5", "3"], ["Netherlands", "58", "21", "10", "12", "9", "1"], ["Austria", "58", "22", "12", "10", "9", "2"], ["Poland", "70", "17", "9", "3", "6", "0"], ["Portugal", "62", "20", "8", "7", "8", "1"], ["Romania", "80", "10", "3", "2", "6", "1"], ["Slovenia", "81", "10", "4", "3", "4", "0"], ["Slovakia", "75", "13", "6", "2", "7", "1"], ["Finland", "71", "19", "6", "6", "4", "0"], ["Sweden", "58", "22", "10", "7", "7", "3"], ["United Kindgom", "53", "25", "10", "9", "12", "2"]], "labels": {"name": "Country", "values": ["European Union", "Belgium", "Bulgaria", "Czech Republic", "Denmark", "Germany", "Estonia", "Ireland", "Greece", "Spain", "France", "Croatia", "Italy", "Cyprus", "Latvia", "Lithuania", "Luxembourg", "Hungary", "Malta", "Netherlands", "Austria", "Poland", "Portugal", "Romania", "Slovenia", "Slovakia", "Finland", "Sweden", "United Kindgom"]}, "metadata": {"link": "https://ec.europa.eu/digital-single-market/en/news/flash-eurobarometer-illegal-content", "type": "Problem", "unit": "Percent (%)", "year": "2018", "title": "Action Taken After Encountering Illegal Content (By Country)", "topic": "Illegal Content", "method": "Survey (N=18313)", "source": "European Commission. Flash Eurobarometer 469 Report: Illegal Content Online (Brussels: European Commission, 2018)", "sub_topic": "Prevalence of illegal content", "chart_number": "92", "geographical": "European Union"}, "description": "The chart shows that most users took not action after encountering illegal content online, although respondents from Germany were the least likely to report having taken no action. The chart results are based on the answers to the question: What action did you take after encountering this content?, \" for which the selection of more than one answer is possible. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020. "},
{"data": [{"data": [45, 20, 7, 8, 11, 22], "name": "European Union"}], "_data": [["Label", "European Union"], ["It was taken-down", "45"], ["It was kept online", "20"], ["It was kept online but with more restricted access", "7"], ["It was kept online but was slightly modified", "8"], ["Other", "11"], ["Don't know", "22"]], "labels": {"name": "Label", "values": ["It was taken-down", "It was kept online", "It was kept online but with more restricted access", "It was kept online but was slightly modified", "Other", "Don't know"]}, "metadata": {"link": "https://ec.europa.eu/digital-single-market/en/news/flash-eurobarometer-illegal-content", "type": "Problem", "unit": "Per cent (%)", "year": "2018", "title": "What Happened to Reported Content (2018)", "topic": "Illegal Content", "method": "Survey (N=7279)", "source": "European Commission. Flash Eurobarometer 469 Report: Illegal Content Online (Brussels: European Commission, 2018)", "sub_topic": "Prevalence of illegal content", "chart_number": "93", "geographical": "European Union"}, "description": "The chart shows that 45% of respondents who took action after encountering illegal content online reported that the content was taken down, but 20% reported that it was kept online without changes. The participants have answered to the question \"What happened to reported content?\" for which multiple answers are possible. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [45, 43, 44, 54, 34, 44, 31, 45, 44, 38, 50, 43, 46, 50, 43, 51, 57, 61, 41, 43, 53, 43, 58, 57, 42, 47, 44, 43, 39], "name": "It was taken-down"}, {"data": [20, 18, 21, 15, 24, 23, 15, 19, 22, 28, 20, 18, 16, 18, 20, 21, 19, 20, 26, 17, 17, 20, 10, 15, 24, 15, 16, 26, 16], "name": "It was kept online"}, {"data": [8, 11, 7, 8, 6, 9, 5, 11, 12, 12, 6, 11, 6, 3, 11, 10, 16, 8, 9, 8, 8, 7, 6, 10, 7, 9, 15, 4, 5], "name": "It was kept online but slightly modified"}, {"data": [7, 7, 8, 11, 3, 8, 8, 11, 8, 5, 8, 8, 4, 8, 6, 9, 12, 5, 6, 4, 8, 11, 6, 6, 4, 7, 10, 4, 8], "name": "It was kept online with more restricted access"}, {"data": [11, 20, 14, 10, 17, 9, 21, 11, 5, 7, 13, 10, 7, 15, 12, 10, 10, 12, 12, 18, 5, 9, 4, 13, 9, 17, 10, 13, 17], "name": "Other"}, {"data": [22, 15, 17, 14, 24, 23, 28, 27, 18, 20, 20, 15, 26, 9, 21, 7, 11, 10, 11, 20, 23, 19, 22, 9, 18, 17, 19, 23, 28], "name": "Don't know"}], "_data": [["Country", "It was taken-down", "It was kept online", "It was kept online but slightly modified", "It was kept online with more restricted access", "Other", "Don't know"], ["European Union", "45", "20", "8", "7", "11", "22"], ["Belgium", "43", "18", "11", "7", "20", "15"], ["Bulgaria", "44", "21", "7", "8", "14", "17"], ["Czech Republic", "54", "15", "8", "11", "10", "14"], ["Denmark", "34", "24", "6", "3", "17", "24"], ["Germany", "44", "23", "9", "8", "9", "23"], ["Estonia", "31", "15", "5", "8", "21", "28"], ["Ireland", "45", "19", "11", "11", "11", "27"], ["Greece", "44", "22", "12", "8", "5", "18"], ["Spain", "38", "28", "12", "5", "7", "20"], ["France", "50", "20", "6", "8", "13", "20"], ["Croatia", "43", "18", "11", "8", "10", "15"], ["Italy", "46", "16", "6", "4", "7", "26"], ["Cyprus", "50", "18", "3", "8", "15", "9"], ["Lithuania", "43", "20", "11", "6", "12", "21"], ["Latvia", "51", "21", "10", "9", "10", "7"], ["Luxembourg", "57", "19", "16", "12", "10", "11"], ["Hungary", "61", "20", "8", "5", "12", "10"], ["Malta", "41", "26", "9", "6", "12", "11"], ["Netherlands", "43", "17", "8", "4", "18", "20"], ["Austria", "53", "17", "8", "8", "5", "23"], ["Poland", "43", "20", "7", "11", "9", "19"], ["Portugal", "58", "10", "6", "6", "4", "22"], ["Romania", "57", "15", "10", "6", "13", "9"], ["Slovenia", "42", "24", "7", "4", "9", "18"], ["Slovakia", "47", "15", "9", "7", "17", "17"], ["Finland", "44", "16", "15", "10", "10", "19"], ["Sweden", "43", "26", "4", "4", "13", "23"], ["United Kingdom", "39", "16", "5", "8", "17", "28"]], "labels": {"name": "Country", "values": ["European Union", "Belgium", "Bulgaria", "Czech Republic", "Denmark", "Germany", "Estonia", "Ireland", "Greece", "Spain", "France", "Croatia", "Italy", "Cyprus", "Lithuania", "Latvia", "Luxembourg", "Hungary", "Malta", "Netherlands", "Austria", "Poland", "Portugal", "Romania", "Slovenia", "Slovakia", "Finland", "Sweden", "United Kingdom"]}, "metadata": {"link": "https://ec.europa.eu/digital-single-market/en/news/flash-eurobarometer-illegal-content", "type": "Problem", "unit": "Share of respondents", "year": "2018", "title": "What Happened to Reported Content Across European Union Member States (2018)", "topic": "Illegal Content", "method": "Survey (N=7279)", "source": "European Commission. Flash Eurobarometer 469 Report: Illegal Content Online (Brussels: European Commission, 2018)", "sub_topic": "Prevalence of illegal content", "chart_number": "94", "geographical": "European Union"}, "description": "The chart shows that, among respondents who took action after encountering illegal content online, respondents from Hungary were the most likely to report that the content was taken down, while respondents from Estonia were the least likely to do so. The participants have answered to the question \"What happened to reported content?\" for which multiple answers are possible. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [19, 45, 18, 12, 6], "name": "European Union"}], "_data": [["Label", "European Union"], ["Very satisfied", "19%"], ["Somewhat satisfied", "45%"], ["Somewhat dissatisfied", "18%"], ["Very dissatisfied", "12%"], ["Don't know", "6%"]], "labels": {"name": "Label", "values": ["Very satisfied", "Somewhat satisfied", "Somewhat dissatisfied", "Very dissatisfied", "Don't know"]}, "metadata": {"link": "https://ec.europa.eu/digital-single-market/en/news/flash-eurobarometer-illegal-content", "type": "Problem", "year": "2018", "title": "Satisfaction with How the Internet Hosting Services Handled a Notification", "topic": "Copyright Infringement", "method": "Survey", "source": "European Commission. Flash Eurobarometer 469 Report: Illegal Content Online (Brussels: European Commission, 2018)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "95", "geographical": "European Union"}, "description": "This pie chart shows that the majority of respondents who encountered illegal content online and informed the internet service hosting the content were satisfied with how the internet hosting service handled their notification. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [58, 59, 69, 38], "name": "Totally agree"}, {"data": [28, 26, 21, 37], "name": "Tend to agree"}, {"data": [5, 6, 4, 10], "name": "Tend to disagree"}, {"data": [4, 4, 3, 9], "name": "Totally disagree"}, {"data": [5, 5, 3, 6], "name": "Don't know"}], "_data": [["Label", "Totally agree", "Tend to agree", "Tend to disagree", "Totally disagree", "Don't know"], ["Internet hosting services should process all notifications they receive and assess the legality of the content", "58%", "28%", "5%", "4%", "5%"], ["Internet hosting services should immediately remove content flagged as illegal by organisations with proven expertise on the topic", "59%", "26%", "6%", "4%", "5%"], ["Internet hosting services should immediately remove content flagged as illegal by public or law enforcement authorities", "69%", "21%", "4%", "3%", "3%"], ["When the internet hosting service removes content uploaded by a user, the user should be able to appeal the decision", "38%", "37%", "10%", "9%", "6%"]], "labels": {"name": "Label", "values": ["Internet hosting services should process all notifications they receive and assess the legality of the content", "Internet hosting services should immediately remove content flagged as illegal by organisations with proven expertise on the topic", "Internet hosting services should immediately remove content flagged as illegal by public or law enforcement authorities", "When the internet hosting service removes content uploaded by a user, the user should be able to appeal the decision"]}, "metadata": {"link": "https://ec.europa.eu/digital-single-market/en/news/flash-eurobarometer-illegal-content", "type": "Problem", "unit": "Percent (%)", "year": "2018", "title": "Perceptions on How Internet Hosting Services Should Deal with Illegal Content Uploaded or Posted by Their Users ", "topic": "Copyright Infringement", "method": "Survey (N=33244)", "source": "European Commission. Flash Eurobarometer 469 Report: Illegal Content Online (Brussels: European Commission, 2018)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "96", "geographical": "European Union"}, "description": "This segmented bar graph shows that the majority of respondents believe that hosting services should immediately remove content flagged as illegal by law enforcement authorities, process all notifications they receive, remove content flagged as illegal by organisations with proven expertise on the topic, and give users the ability to appeal removal decisions. The results are based on the answers to the question \"Do you agree or disagree with each of the following statements?\""},
{"data": [{"data": [9, 7.2, 5.8, 6.9, 11.4, 13.3, 8.6, 9.5, 12.4, 5.4, 5, null, null, null, null], "name": "Child nudity and sexual exploaitation"}, {"data": [null, null, null, null, null, null, null, null, null, null, null, 2.3, 1.8, 1.8, 2.1], "name": "Child endangerment: Nudity and Physical Abuse"}, {"data": [null, null, null, null, null, null, null, null, null, null, null, 25.6, 21.2, 19.8, 16.5], "name": "Child endangerment: Sexual Exploitation"}], "_data": [["Period", "Child nudity and sexual exploaitation", "Child endangerment: Nudity and Physical Abuse", "Child endangerment: Sexual Exploitation"], ["July-September 2018", "9"], ["October-December 2018", "7.2"], ["January-March 2019", "5.8"], ["April-June 2019", "6.9"], ["July-September 2019", "11.4"], ["October-December 2019", "13.3"], ["January-March 2020", "8.6"], ["April-June 2020", "9.5"], ["July-September 2020", "12.4"], ["October-December 2020", "5.4"], ["January-March 2021", "5"], ["April-June 2021", "", "2.3", "25.6"], ["July-September 2021", "", "1.8", "21.2"], ["October-December 2021", "", "1.8", "19.8"], ["January-March 2022", "", "2.1", "16.5"]], "labels": {"name": "Period", "values": ["July-September 2018", "October-December 2018", "January-March 2019", "April-June 2019", "July-September 2019", "October-December 2019", "January-March 2020", "April-June 2020", "July-September 2020", "October-December 2020", "January-March 2021", "April-June 2021", "July-September 2021", "October-December 2021", "January-March 2022"]}, "metadata": {"link": "https://transparency.fb.com/data/community-standards-enforcement/child-nudity-and-sexual-exploitation/facebook/", "type": "Problem", "unit": "Pieces of content acted on (million)", "year": "2018-2022", "title": "Content Actioned Under Child Nudity and Sexual Exploatation Violations on Facebook", "topic": "Illegal Content", "method": "Self-reporting", "source": "Meta. Transparency Report: Child Endangerment: Nudity and Physical Abuse and Sexual Exploitation (June 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "97", "geographical": "Global"}, "description": "The chart shows the number of content actioned under child nudity and sexual exploatation violations on Facebook, from third quarter of 2018 until the first quarter of 2022. Since the April 2021, the Child Nudity and Sexual Exploitation content was renamed <b>Child Endangerment</b>, and includes two distinct topics - <i>Nudity and Physical Abuse</i> and <i>Sexual Exploitations</i>, which are monitored separately. The data from the second and third quarters of 2021 shows that volume of content actioned for sexual exploitation violations is significantly higher (ten times higher) than the content actioned for nudity and physical abuse. In the first quarter of 2022, the volume of content actioned for sexual exploitation violations decreased by 35.5% compared to the second quarter of 2021, but it remains significantly higher than the content actioned for nudity and physical abuse."},
{"data": [{"data": [99.12, 99.28, 99.1, 99.2, 99.5, 99.7, 99.5, 99.2, 99.4, 98.8, 98.9], "name": "Percentage of content actioned that Facebook found and flagged before users reported it"}, {"data": [0.88, 0.72, 0.9, 0.8, 0.5, 0.3, 0.5, 0.8, 0.6, 1.2, 1.1], "name": "Percentage of content actioned that users reported first"}], "_data": [["Period", "Percentage of content actioned that Facebook found and flagged before users reported it", "Percentage of content actioned that users reported first"], ["July-September 2018", "99.12", "0.88"], ["October-December 2018", "99.28", "0.72"], ["January-March 2019", "99.1", "0.9"], ["April-June 2019", "99.2", "0.8"], ["July-September 2019", "99.5", "0.5"], ["October-December 2019", "99.7", "0.3"], ["January-March 2020", "99.5", "0.5"], ["April-June 2020", "99.2", "0.8"], ["July-September 2020", "99.4", "0.6"], ["October-December 2020", "98.8", "1.2"], ["January-March 2021", "98.9", "1.1"]], "labels": {"name": "Period", "values": ["July-September 2018", "October-December 2018", "January-March 2019", "April-June 2019", "July-September 2019", "October-December 2019", "January-March 2020", "April-June 2020", "July-September 2020", "October-December 2020", "January-March 2021"]}, "metadata": {"link": "https://transparency.fb.com/data/community-standards-enforcement/child-nudity-and-sexual-exploitation/facebook/", "type": "Problem", "unit": "Per Cent (%)", "year": "2018-2021", "title": "Percentage of Content Found by Facebook as Containing Child Nudity and Sexual Exploitation Compared to the Content Reported by the Users", "topic": "Illegal Content", "method": "Self-reporting", "source": "Facebook. Transparency Report: Percentage of Content Actioned Under Child Nudity and Sexual Exploatation Violations on Facebook (facebook.com, 2021)", "sub_topic": "Prevalence of illegal content", "chart_number": "98", "geographical": "Global"}, "description": "This chart shows the percentage of content found by Facebook as containing child nudity and exploitation compared to the content reported by the users from July 2018 until March 2021. Since April 2021, the Child Nudity and Sexual Exploitation content have been renamed Child Endangerment, with two categories: Nudity and Physical Abuse and Sexual Exploitations, which are monitored separately. The data shows that the share of content reported by users (around 1%) is significantly lower that the one found by Facebook."},
{"data": [{"data": [279550, 835731, 807676, 756933, 851441, 931438, 1482109, 3820637, 2494698, 3826486, 5131470, 1874729, 1986073, 1182403, 968178], "name": "Number of videos removed"}], "_data": [["Period", "Number of videos removed"], ["September 2018", "279550"], ["October-December 2018", "835731"], ["January-March 2019", "807676"], ["April-June 2019", "756933"], ["July-September 2019", "851441"], ["October-December 2019", "931438"], ["January-March 2020", "1482109"], ["April-June 2020", "3820637"], ["July-September 2020", "2494698"], ["October-December 2020", "3826486"], ["January-March 2021", "5131470"], ["April-June 2021", "1874729"], ["July-September 2021", "1986073"], ["October-December 2021", "1182403"], ["January-March 2022", "968178"]], "labels": {"name": "Period", "values": ["September 2018", "October-December 2018", "January-March 2019", "April-June 2019", "July-September 2019", "October-December 2019", "January-March 2020", "April-June 2020", "July-September 2020", "October-December 2020", "January-March 2021", "April-June 2021", "July-September 2021", "October-December 2021", "January-March 2022"]}, "metadata": {"link": "https://transparencyreport.google.com/youtube-policy/featured-policies/child-safety", "type": "Problem", "unit": "Number of videos removed", "year": "2018-2022", "title": "Number of Videos Removed by Google Under Their Child Safety Policy", "topic": "Illegal Content", "method": "Self-reporting", "source": "Google. Transparency Report: Child Safety Policy (www.google.com, 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "99", "geographical": "Global"}, "description": "The chart shows the number of videos removed by Google under their Child Safety policy, starting from September 2018. The latest available data shows that overall the number of videos removed under the Child Safety Policy declined in in the first quarter of 2022 by 81% compared to the same period of the previous year. Compared to the previous quarter, the change is considerably lower, declining only by 18% in the first quarter of 2022 compared to the previous one. "},
{"data": [{"data": [20, 69], "name": "Top down"}, {"data": [80, 31], "name": "Bottom up"}], "_data": [["Share of content", "Top down", "Bottom up"], ["Share of total sample (both social and traditional media content)", "20", "80"], ["Share of engagements (within social media content)", "69", "31"]], "labels": {"name": "Share of content", "values": ["Share of total sample (both social and traditional media content)", "Share of engagements (within social media content)"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/types-sources-and-claims-covid-19-misinformation", "type": "Problem", "unit": "Per Cent (%)", "year": "2020", "title": "Top-Down Versus Bottom-Up Misinformation", "topic": "Disinformation", "method": "Survey ", "source": "Brennen, J. Scott, Felix Simon, Philip N. Howard, Rasmus Kleis Nielsen. \"Types, Sources and Claims of COVID-19 Misinformation,\" Reuters Institute, 07 April 2020", "sub_topic": "Prevalence of illegal content", "chart_number": "100", "geographical": "European Union"}, "description": "The chart shows that high-level politicians, celebrities, or other prominent public figures produced or spread only 20% of the misinformation in Reuters Institute's sample, but that misinformation attracted a large majority of all social media engagements in the sample. The first bar shows the share of content that was produced or shared by prominent persons in the whole sample (N=225). The second bar shows the per cent of total social media engagements of content from prominent persons out of the sub-sample of social media posts with available engagement data (N=145)."},
{"data": [{"data": [29], "name": "Misleading content"}, {"data": [24], "name": "False context"}, {"data": [6], "name": "Manipulated content"}, {"data": [30], "name": "Fabricated content"}, {"data": [8], "name": "Imposter content"}, {"data": [3], "name": "Satire/parody "}], "_data": [["Type of content", "Misleading content", "False context", "Manipulated content", "Fabricated content", "Imposter content", "Satire/parody "], ["Share of content", "29", "24", "6", "30", "8", "3"]], "labels": {"name": "Type of content", "values": ["Share of content"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/types-sources-and-claims-covid-19-misinformation", "type": "Problem", "year": "2020", "title": "Reconfigured versus Fabricated Misinformation", "topic": "Disinformation", "method": "Survey ", "source": "Brennen, J. Scott, Felix Simon, Philip N. Howard, Rasmus Kleis Nielsen. \"Types, Sources and Claims of COVID-19 Misinformation,\" Reuters Institute, 07 April 2020", "sub_topic": "Prevalence of illegal content", "chart_number": "101", "geographical": "European Union"}, "description": "The chart shows the proportion of reconfigured (N=133) and fabricated (N=86) misinformation in the sample (N=225) and the types of misinformation that constitute both reconfigured and fabricated misinformation. Out of the share of the content showed above, 59% is reconfigured (out of misleading, false context and manipulated content) and 38% is fabricated (out of fabricated and imposter content)."},
{"data": [{"data": [59, 27, 24], "name": "Percentage of posts rated as false still active with no clear warning on each platform"}], "_data": [["Online platform", "Percentage of posts rated as false still active with no clear warning on each platform"], ["Twitter", "59"], ["YouTube", "27"], ["Facebook", "24"]], "labels": {"name": "Online platform", "values": ["Twitter", "YouTube", "Facebook"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/types-sources-and-claims-covid-19-misinformation", "type": "Problem", "unit": "Per Cent (%)", "year": "2020", "title": "Percentage of Active False Posts With No Direct Warning Label ", "topic": "Disinformation", "method": "Survey ", "source": "Brennen, J. Scott, Felix Simon, Philip N. Howard, Rasmus Kleis Nielsen. \"Types, Sources and Claims of COVID-19 Misinformation,\" Reuters Institute, 07 April 2020", "sub_topic": "Prevalence of illegal content", "chart_number": "102", "geographical": "European Union"}, "description": "The chart shows the percentage of posts in Reuters Institute's sample rated as false that were still active and did not have a clear label at the end of March 2020 (Twitter: (N=43; YouTube: N=6; Facebook: N=33) out of the total number of posts on each platform in the sample (Twitter: N= 73; YouTube: N= 22; Facebook: N=137)."},
{"data": [{"data": [23.6, 38, 52.9, 52.9, 60.7, 68.7, 71.1, 80.6, 80.7, 89.1, 94.7, 94.7, 97, 96.7, 97.6, 96.5, 95.9, 95.6], "name": "Percentage of content actioned that Facebook found and flagged before users reported it"}, {"data": [76.4, 62, 47.1, 47.1, 39.3, 31.3, 28.9, 19.4, 19.3, 10.9, 5.3, 5.3, 3, 3.3, 2.4, 3.5, 4.1, 4.4], "name": "Percentage of content actioned that users reported first"}], "_data": [["Period", "Percentage of content actioned that Facebook found and flagged before users reported it", "Percentage of content actioned that users reported first"], ["October-December 2017", "23.6", "76.4"], ["January-March 2018", "38", "62"], ["April-June 2018", "52.9", "47.1"], ["July-September 2018", "52.9", "47.1"], ["October-December 2018", "60.7", "39.3"], ["January-March 2019", "68.7", "31.3"], ["April-June 2019", "71.1", "28.9"], ["July-September 2019", "80.6", "19.4"], ["October-December 2019", "80.7", "19.3"], ["January-March 2020", "89.1", "10.9"], ["April-June 2020", "94.7", "5.3"], ["July-September 2020", "94.7", "5.3"], ["October-December 2020", "97", "3"], ["January-March 2021", "96.7", "3.3"], ["April-June 2021", "97.6", "2.4"], ["July-September 2021", "96.5", "3.5"], ["October-December 2021", "95.9", "4.1"], ["January-March 2022", "95.6", "4.4"]], "labels": {"name": "Period", "values": ["October-December 2017", "January-March 2018", "April-June 2018", "July-September 2018", "October-December 2018", "January-March 2019", "April-June 2019", "July-September 2019", "October-December 2019", "January-March 2020", "April-June 2020", "July-September 2020", "October-December 2020", "January-March 2021", "April-June 2021", "July-September 2021", "October-December 2021", "January-March 2022"]}, "metadata": {"link": "https://transparency.fb.com/data/community-standards-enforcement/hate-speech/facebook/", "type": "Problem", "unit": "Per Cent (%)", "year": "2017-2022", "title": "Percentage of Content Found by Facebook as Containing Hate Speech Compared to the Content Reported by the Users", "topic": "Hate Speech", "method": "Self-reporting", "source": "Meta. Transparency Report: Hate Speech (June 2022)", "sub_topic": "Prevalence of hate speech", "chart_number": "105", "geographical": "Global"}, "description": "This chart shows the percentage of content found by Facebook as containing hate speech compared to the content reported by the users, over the period October 2017 - March 2022. The percentage of content found by Facebook has significantly increased compared to the one reported at the begining of the monitoring period (2017), which let to a decrease of the share of content reported by users."},
{"data": [{"data": [52, 49, 35, 20, 19, 17, 14, 11, 28], "name": "A very big problem"}, {"data": [33, 41, 42, 40, 41, 44, 30, 37, 38], "name": "A fairly big problem"}, {"data": [11, 7, 19, 35, 38, 34, 47, 47, 30], "name": "Not a very big problem"}, {"data": [4, 3, 2, 5, 1, 4, 7, 5, 4], "name": "Not a problem at all"}, {"data": [1, null, 2, 1, 1, 2, 2, 1, 1], "name": "Don't know"}], "_data": [["Country", "A very big problem", "A fairly big problem", "Not a very big problem", "Not a problem at all", "Don't know"], ["France", "52", "33", "11", "4", "1"], ["Hungary", "49", "41", "7", "3", "0"], ["Belgium", "35", "42", "19", "2", "2"], ["Sweden", "20", "40", "35", "5", "1"], ["Italy", "19", "41", "38", "1", "1"], ["Germany", "17", "44", "34", "4", "2"], ["Latvia", "14", "30", "47", "7", "2"], ["United Kingdom", "11", "37", "47", "5", "1"], ["Eight-country average", "28", "38", "30", "4", "1"]], "labels": {"name": "Country", "values": ["France", "Hungary", "Belgium", "Sweden", "Italy", "Germany", "Latvia", "United Kingdom", "Eight-country average"]}, "metadata": {"link": "https://fra.europa.eu/sites/default/files/fra-2013-discrimination-hate-crime-against-jews-eu-member-states-0_en.pdf", "type": "Problem", "unit": "Share of respondents (%)", "year": "2013", "title": "Perception of Antisemitism in Eight European Union Member States ", "topic": "Hate Speech", "method": "Survey (N = 5847)", "source": "European Union Agency for Fundamental Rights. Discrimination and Hate Crime Against Jews in European Union Member States: Experiences and Perceptions of Anti-Semitism (Luxembourg: Publications Office of the European Union, 2014)", "sub_topic": "Prevalence of hate speech", "chart_number": "107", "geographical": "European Union"}, "description": "The chart illustrates the perception respondents from eight European Union member states about the antisemitism seen as a problem. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020. The results show that antisemitism is seen as a very big problem in France and Hungary and not a very big problem in United Kingdom and Latvia."},
{"data": [{"data": [9, 27, 27, 32, 37, 44, 58, 70, 74], "name": "Increased a lot"}, {"data": [30, 41, 39, 36, 43, 32, 30, 21, 14], "name": "Increased a little"}, {"data": [44, 25, 27, 23, 14, 18, 9, 5, 8], "name": "Stayed the same"}, {"data": [5, 6, 4, 3, 2, 3, 1, 1, 1], "name": "Decreased a little"}, {"data": [4, 1, 1, 3, 1, 2, 1, 3, 3], "name": "Decreased a lot"}, {"data": [9, 1, 3, 4, 2, 2, 2, 1, 1], "name": "Don't know"}], "_data": [["Country", "Increased a lot", "Increased a little", "Stayed the same", "Decreased a little", "Decreased a lot", "Don't know"], ["Latvia", "9", "30", "44", "5", "4", "9"], ["Italy", "27", "41", "25", "6", "1", "1"], ["United Kingdom", "27", "39", "27", "4", "1", "3"], ["Germany", "32", "36", "23", "3", "3", "4"], ["Sweden", "37", "43", "14", "2", "1", "2"], ["Eight-country average", "44", "32", "18", "3", "2", "2"], ["Belgium", "58", "30", "9", "1", "1", "2"], ["Hungary", "70", "21", "5", "1", "3", "1"], ["France", "74", "14", "8", "1", "3", "1"]], "labels": {"name": "Country", "values": ["Latvia", "Italy", "United Kingdom", "Germany", "Sweden", "Eight-country average", "Belgium", "Hungary", "France"]}, "metadata": {"link": "https://fra.europa.eu/sites/default/files/fra-2013-discrimination-hate-crime-against-jews-eu-member-states-0_en.pdf", "type": "Problem", "unit": "Percent (%)", "year": "2013", "title": "Perceptions on Changes in the Level of Antisemitism over the Past Five Years, Across Eight European Union Member States ", "topic": "Hate Speech", "method": "Survey (N = 5847)", "source": "European Union Agency for Fundamental Rights. Discrimination and Hate Crime Against Jews in European Union Member States: Experiences and Perceptions of Anti-Semitism (Luxembourg: Publications Office of the European Union, 2014)", "sub_topic": "Prevalence of hate speech", "chart_number": "108", "geographical": "European Union"}, "description": "The chart presents the perception on proliferation of antisemitism within eight European Union member states, between 2008-2013. The results show that while in France for 74% respondents this perception increased a lot, in as Latvia, the majority of respondents (44%) considered that it stayed the same. "},
{"data": [{"data": [46, 27, 24, 19, 17, 16, 15], "name": "A very big problem"}, {"data": [29, 32, 30, 31, 27, 29, 30], "name": "A fairly big problem"}, {"data": [13, 30, 35, 37, 39, 41, 41], "name": "Not a very big problem"}, {"data": [3, 10, 9, 8, 14, 8, 9], "name": "Not a problem at all"}, {"data": [9, 2, 2, 5, 3, 5, 5], "name": "Don't know"}], "_data": [["Social and /or political issue", "A very big problem", "A fairly big problem", "Not a very big problem", "Not a problem at all", "Don't know"], ["Antisemitism on the internnet", "46", "29", "13", "3", "9"], ["Antisemitism in the media", "27", "32", "30", "10", "2"], ["Expressions of hostility towards Jews in the street or other public places", "24", "30", "35", "9", "2"], ["Desecration of Jewish cemeteries", "19", "31", "37", "8", "5"], ["Antisemitism in political life", "17", "27", "39", "14", "3"], ["Vandalism of Jewish buildings or institutions", "16", "29", "41", "8", "5"], ["Antisemitic graffiti", "15", "30", "41", "9", "5"]], "labels": {"name": "Social and /or political issue", "values": ["Antisemitism on the internnet", "Antisemitism in the media", "Expressions of hostility towards Jews in the street or other public places", "Desecration of Jewish cemeteries", "Antisemitism in political life", "Vandalism of Jewish buildings or institutions", "Antisemitic graffiti"]}, "metadata": {"link": "https://fra.europa.eu/sites/default/files/fra-2013-discrimination-hate-crime-against-jews-eu-member-states-0_en.pdf", "type": "Problem", "unit": "Share of respondents (%)", "year": "2013", "title": "Assessment of Manifestations of Anti-Semitism Against Jewish Community (Average of Selected European Union Member States)", "topic": "Hate Speech", "method": "Survey (N = 5847)", "source": "European Union Agency for Fundamental Rights. Discrimination and Hate Crime Against Jews in European Union Member States: Experiences and Perceptions of Anti-Semitism (Luxembourg: Publications Office of the European Union, 2014)", "sub_topic": "Prevalence of hate speech", "chart_number": "109", "geographical": "European Union"}, "description": "The chart illustrates the assessment of respondents to the various acts of anti-Semitism against the Jewish community in 2013, at the level of the countries surveyed (eight European Union member states). European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020. Among the specific manifestations, three quarters of all respondents (75%) consider the online antisemitism as a particular problem (either \"a very big\" or a \"fairly big problem\")."},
{"data": [{"data": [85, 70, 74, 42, 52, 54, 51], "name": "Belgium"}, {"data": [67, 40, 48, 46, 30, 33, 30], "name": "Germany"}, {"data": [85, 71, 84, 74, 69, 78, 50], "name": "France"}, {"data": [86, 73, 72, 79, 69, 52, 84], "name": "Hungary"}, {"data": [87, 59, 30, 41, 61, 43, 36], "name": "Italy"}, {"data": [52, 37, 16, 56, 21, 23, 35], "name": "Latvia"}, {"data": [68, 54, 51, 34, 29, 30, 41], "name": "Sweden"}, {"data": [64, 52, 35, 35, 26, 31, 34], "name": "United Kingdom"}, {"data": [75, 59, 54, 50, 45, 45, 44], "name": "Eight-country average"}], "_data": [["Social and /or political issue", "Belgium", "Germany", "France", "Hungary", "Italy", "Latvia", "Sweden", "United Kingdom", "Eight-country average"], ["Antisemitism on the internet", "85", "67", "85", "86", "87", "52", "68", "64", "75"], ["Antisemitism in the media", "70", "40", "71", "73", "59", "37", "54", "52", "59"], ["Expressions of hostility towards Jews in the street or other public places", "74", "48", "84", "72", "30", "16", "51", "35", "54"], ["Desecration of Jewish cemeteries", "42", "46", "74", "79", "41", "56", "34", "35", "50"], ["Antisemitic graffiti", "52", "30", "69", "69", "61", "21", "29", "26", "45"], ["Vandalism of Jewish buildings or institutions", "54", "33", "78", "52", "43", "23", "30", "31", "45"], ["Antisemitism in political life", "51", "30", "50", "84", "36", "35", "41", "34", "44"]], "labels": {"name": "Social and /or political issue", "values": ["Antisemitism on the internet", "Antisemitism in the media", "Expressions of hostility towards Jews in the street or other public places", "Desecration of Jewish cemeteries", "Antisemitic graffiti", "Vandalism of Jewish buildings or institutions", "Antisemitism in political life"]}, "metadata": {"link": "https://fra.europa.eu/sites/default/files/fra-2013-discrimination-hate-crime-against-jews-eu-member-states-0_en.pdf", "type": "Problem", "year": "2013", "title": "Assessment of Manifestations of Anti-Semitism Against Jewish Community Across European Union Countries", "topic": "Hate Speech", "method": "Survey (N=5847)", "source": "European Union Agency for Fundamental Rights. Discrimination and Hate Crime Against Jews in European Union Member States: Experiences and Perceptions of Anti-Semitism (Luxembourg: Publications Office of the European Union, 2014)", "sub_topic": "Prevalence of hate speech", "chart_number": "110", "geographical": "European Union"}, "description": "The table presents the share of respondents from eight European Union member states that assessed as a problem different manifestations of anti-Semitism against Jewish community in 2013. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020. The results show that more than half of the respondents in each country surveyed consider \"antisemitism on the internet\" as being a problem. For each country, the three most serious manifestations of antisemitism - as assessed by the respondents, are antisemitism on the Internet, in the media and expression of hostility towards Jews in the streets or other public places. The question asked was \"In your opinion, how big a problem, if at all, are each of the following in [COUNTRY] today?\" Answers include both \"a very big problem\" and \"a fairly big problem.\" The items are listed in descending order according to the average of the eight countries. "},
{"data": [{"data": [83, 64, 69, 55], "name": "Belgium"}, {"data": [71, 41, 54, 39], "name": "Germany"}, {"data": [87, 70, 72, 87], "name": "France"}, {"data": [86, 71, 76, 87], "name": "Hungary"}, {"data": [88, 53, 65, 47], "name": "Italy"}, {"data": [53, 40, 31, 34], "name": "Latvia"}, {"data": [63, 49, 37, 38], "name": "Sweden"}, {"data": [64, 48, 40, 33], "name": "United Kingdom"}, {"data": [75, 56, 56, 53], "name": "Eight-country average"}], "_data": [["Number of antisemitic comments", "Belgium", "Germany", "France", "Hungary", "Italy", "Latvia", "Sweden", "United Kingdom", "Eight-country average"], ["Antisemitic comments on the internet (including discussion forums, social networking sites)", "83", "71", "87", "86", "88", "53", "63", "64", "75"], ["Antisemitic reporting in the media", "64", "41", "70", "71", "53", "40", "49", "48", "56"], ["Antisemitic comments in discussions people have (such as at the workplace, at school, or elsewhere)", "69", "54", "72", "76", "65", "31", "37", "40", "56"], ["Antisemitic comments in political speeches and discussions", "55", "39", "87", "87", "47", "34", "38", "33", "53"]], "labels": {"name": "Number of antisemitic comments", "values": ["Antisemitic comments on the internet (including discussion forums, social networking sites)", "Antisemitic reporting in the media", "Antisemitic comments in discussions people have (such as at the workplace, at school, or elsewhere)", "Antisemitic comments in political speeches and discussions"]}, "metadata": {"link": "https://fra.europa.eu/sites/default/files/fra-2013-discrimination-hate-crime-against-jews-eu-member-states-0_en.pdf", "type": "Problem", "year": "2013", "title": "Distribution of Respondents Seeing Antisemitic Comments as a Problem in Different Arenas Based On What They Have Seen or Heard in Surveyed European Union Member States (2013)", "topic": "Hate Speech", "method": "Survey (N = 5847)", "source": "European Union Agency for Fundamental Rights. Discrimination and Hate Crime Against Jews in European Union Member States: Experiences and Perceptions of Anti-Semitism (Luxembourg: Publications Office of the European Union, 2014)", "sub_topic": "Prevalence of hate speech", "chart_number": "111", "geographical": "European Union"}, "description": "The table shows the distribution of respondents (as percent) who see antisemitic comments as a problem in different arenas based on what they have seen or heard in the surveyed European Union member states. The European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [61, 36, 48, 18, 23, 20, 21, 14], "name": "Belgium"}, {"data": [49, 28, 42, 25, 24, 15, 29, 11], "name": "Germany"}, {"data": [56, 56, 44, 28, 15, 24, 17, 13], "name": "France"}, {"data": [49, 75, 57, 21, 47, 59, 44, 22], "name": "Hungary "}, {"data": [59, 35, 37, 25, 20, 23, 20, 14], "name": "Italy"}, {"data": [14, 30, 33, 8, 31, 16, 23, 5], "name": "Latvia"}, {"data": [51, 33, 35, 21, 19, 15, 15, 8], "name": "Sweden"}, {"data": [35, 21, 35, 16, 13, 9, 9, 5], "name": "United Kingdom"}, {"data": [48, 38, 37, 22, 21, 21, 20, 11], "name": "Eight-country agerage"}], "_data": [["Survey statements", "Belgium", "Germany", "France", "Hungary ", "Italy", "Latvia", "Sweden", "United Kingdom", "Eight-country agerage"], ["Israelis behave \"like Nazis\" towards the Palestinians", "61", "49", "56", "49", "59", "14", "51", "35", "48"], ["Jews have too much power in [COUNTRY] (economy, politics, media)", "36", "28", "56", "75", "35", "30", "33", "21", "38"], ["Jews exploit Holocaust victimhood for their own purposes", "48", "42", "44", "57", "37", "33", "35", "35", "37"], ["Jews are only a religious group and not a nation", "18", "25", "28", "21", "25", "8", "21", "16", "22"], ["The Holocaust is a myth or has been exaggerated", "23", "24", "15", "47", "20", "31", "19", "13", "21"], ["Jews are responsible for the current economic crisis", "20", "15", "24", "59", "23", "16", "15", "9", "21"], ["The interests of Jews in [COUNTRY] are very different from the interests of the rest of the population", "21", "29", "17", "44", "20", "23", "15", "9", "20"], ["Jews are not capable of integrating\ninto [COUNTRY] society", "14", "11", "13", "22", "14", "5", "8", "5", "11"]], "labels": {"name": "Survey statements", "values": ["Israelis behave \"like Nazis\" towards the Palestinians", "Jews have too much power in [COUNTRY] (economy, politics, media)", "Jews exploit Holocaust victimhood for their own purposes", "Jews are only a religious group and not a nation", "The Holocaust is a myth or has been exaggerated", "Jews are responsible for the current economic crisis", "The interests of Jews in [COUNTRY] are very different from the interests of the rest of the population", "Jews are not capable of integrating\ninto [COUNTRY] society"]}, "metadata": {"link": "https://fra.europa.eu/sites/default/files/fra-2013-discrimination-hate-crime-against-jews-eu-member-states-0_en.pdf", "type": "Problem", "unit": "Percent (%)", "year": "2013", "title": "Distribution of Respondents Hearing or Seeing the Selected Statements Made by non-Jewish People in Surveyed European Union Member States (2013)", "topic": "Hate Speech", "method": "Survey (N = 5847)", "source": "European Union Agency for Fundamental Rights. Discrimination and Hate Crime Against Jews in European Union Member States: Experiences and Perceptions of Anti-Semitism (Luxembourg: Publications Office of the European Union, 2014)", "sub_topic": "Prevalence of hate speech", "chart_number": "112", "geographical": "European Union"}, "description": "This table shows the percentage of respondents who have heard or seen the selected statements made by non-Jewish people, by European Union country. The United Kingdom left the European Union on 31 January 2020. Answers included in the survey are both \"all the time\" and \"frequently\"."},
{"data": [{"data": [20, 64, 55, 35, 49, 28, 79, 33, 50, 38], "name": "Women "}, {"data": [22, 59, 70, 33, 51, 36, 74, 34, 55, 41], "name": "Men"}], "_data": [["Country", "Women ", "Men"], ["Bulgaria", "20", "22"], ["Czech Republic", "64", "59"], ["Greece", "55", "70"], ["Spain", "35", "33"], ["Croatia", "49", "51"], ["Hungary", "28", "36"], ["Portugal", "79", "74"], ["Romania", "33", "34"], ["Slovakia", "50", "55"], ["Total", "38", "41"]], "labels": {"name": "Country", "values": ["Bulgaria", "Czech Republic", "Greece", "Spain", "Croatia", "Hungary", "Portugal", "Romania", "Slovakia", "Total"]}, "metadata": {"link": "https://fra.europa.eu/sites/default/files/fra_uploads/fra-2019-eu-minorities-survey-roma-women_en.pdf", "type": "Problem", "unit": "Percent (%)", "year": "2016", "title": "Distribution of Roma Feeling Discriminated Against When Looking for Work in the Five Years Before the Survey (2016)", "topic": "Hate Speech", "method": "Survey (N =3987)", "source": "European Union Agency for Fundamental Rights. Second European Union Minorities and Discrimination Survey: Roma Women in Nine European Union Member States (Luxembourg: Publications Office of the European Union, 2019)", "sub_topic": "Prevalence of hate speech", "chart_number": "116", "geographical": "European Union"}, "description": "The chart shows the percentages of Roma who felt discriminated against when looking for work, during the reference period 2011 to 2016 in nine European Union member states. The European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [24, 54, 27, 21, 53, 30, 10, 28, 53, 34], "name": "Women "}, {"data": [32, 56, 36, 22, 55, 31, 17, 36, 50, 38], "name": "Men"}], "_data": [["Country", "Women ", "Men"], ["Bulgaria", "24", "32"], ["Czech Republic", "54", "56"], ["Greece", "27", "36"], ["Spain", "21", "22"], ["Croatia", "53", "55"], ["Hungary", "30", "31"], ["Portugal", "10", "17"], ["Romania", "28", "36"], ["Slovakia", "53", "50"], ["Total", "34", "38"]], "labels": {"name": "Country", "values": ["Bulgaria", "Czech Republic", "Greece", "Spain", "Croatia", "Hungary", "Portugal", "Romania", "Slovakia", "Total"]}, "metadata": {"link": "https://fra.europa.eu/sites/default/files/fra_uploads/fra-2019-eu-minorities-survey-roma-women_en.pdf", "type": "Solution", "unit": "Percent (%)", "year": "2016", "title": "Awareness of a Law That Forbids Discrimination Based on Skin Colour, Ethnic Origin or Religion in Nine European Union Member States (2016)", "topic": "Hate Speech", "method": "Survey (N = 7947)", "source": "European Union Agency for Fundamental Rights. Second European Union Minorities and Discrimination Survey: Roma Women in Nine European Union Member States (Luxembourg: Publications Office of the European Union, 2019)", "sub_topic": "Awareness how to fight with hate speech", "chart_number": "117", "geographical": "European Union"}, "description": "The chart shows the level of awareness of Roma communities about a law that forbids discrimination based on skin colour, ethnic origin or religion in nine European Union member states, in 2016. European Union refes to EU28. The United Kingdom left the European Union on 31 January 2020. The results show that, on average, only slightly more than one in three Roma women (34 %) and men (38 %) are aware of the existence of such antidiscrimination legislation in their country."},
{"data": [{"data": [12, 53, 49, 30, 24, 18, 23, 27, 36, 29], "name": "Women "}, {"data": [13, 59, 51, 30, 40, 17, 16, 28, 39, 31], "name": "Men"}], "_data": [["Country", "Women ", "Men"], ["Bulgaria", "12", "13"], ["Czech Republic", "53", "59"], ["Greece", "49", "51"], ["Spain", "30", "30"], ["Croatia", "24", "40"], ["Hungary", "18", "17"], ["Portugal", "23", "16"], ["Romania", "27", "28"], ["Slovakia", "36", "39"], ["Total", "29", "31"]], "labels": {"name": "Country", "values": ["Bulgaria", "Czech Republic", "Greece", "Spain", "Croatia", "Hungary", "Portugal", "Romania", "Slovakia", "Total"]}, "metadata": {"link": "https://fra.europa.eu/sites/default/files/fra_uploads/fra-2019-eu-minorities-survey-roma-women_en.pdf", "type": "Problem", "unit": "Percent (%)", "year": "2016", "title": "Harassment Experienced Due to Roma Background ", "topic": "Hate Speech", "method": "Survey (N = 7947)", "source": "European Union Agency for Fundamental Rights. Second European Union Minorities and Discrimination Survey: Roma Women in Nine European Union Member States (Luxembourg: Publications Office of the European Union, 2019)", "sub_topic": "Prevalence of hate speech", "chart_number": "118", "geographical": "European Union"}, "description": "The chart presents the shares of Roma respondents that have experienced some form of harassment due to their ethnic origin in the 12 months before the survey. The results show that, in 2016, almost every third Roma survey respondent (31% of men and 29% of women) believed that they had experienced, at least once, some form of ethnic-based harassment during the previous year."},
{"data": [{"data": [79.64, 70.37, 86.54, 81.42, 70.62, 76.83, 87.15, 93.12, 90.79, 93.46, 95.16, 93.88, 94.4, 95, 94.4, 94.72, 91.94, 91.28], "name": "Automated flagging"}, {"data": [20.36, 29.63, 13.46, 18.58, 29.38, 23.17, 12.85, 6.88, 9.21, 6.54, 4.84, 6.12, 5.6, 5, 5.6, 5.28, 8.06, 8.72], "name": "Human detection"}], "_data": [["Period", "Automated flagging", "Human detection"], ["October - December 2017", "79.64%", "20.36%"], ["January - March 2018", "70.37%", "29.63%"], ["April - June 2018", "86.54%", "13.46%"], ["July - September 2018", "81.42%", "18.58%"], ["October - December 2018", "70.62%", "29.38%"], ["January - March 2019", "76.83%", "23.17%"], ["April - June 2019", "87.15%", "12.85%"], ["July - September 2019", "93.12%", "6.88%"], ["October - December 2019", "90.79%", "9.21%"], ["January - March 2020", "93.46%", "6.54%"], ["April - June 2020", "95.16%", "4.84%"], ["July - September 2020", "93.88%", "6.12%"], ["October - December 2020", "94.40%", "5.60%"], ["January - March 2021", "95.00%", "5.00%"], ["April-June 2021", "94.40%", "5.60%"], ["July-September 2021", "94.72%", "5.28%"], ["October - December 2021", "91.94%", "8.06%"], ["January - March 2022", "91.28%", "8.72%"]], "labels": {"name": "Period", "values": ["October - December 2017", "January - March 2018", "April - June 2018", "July - September 2018", "October - December 2018", "January - March 2019", "April - June 2019", "July - September 2019", "October - December 2019", "January - March 2020", "April - June 2020", "July - September 2020", "October - December 2020", "January - March 2021", "April-June 2021", "July-September 2021", "October - December 2021", "January - March 2022"]}, "metadata": {"link": "https://transparencyreport.google.com/youtube-policy/removals?hl=en", "type": "Problem", "unit": "Per cent of removed videos ", "year": "2017-2022", "title": "Videos Removed by YouTube, by Source of First Detection", "topic": "Illegal Content", "method": "Self-reporting", "source": "Google. Transparency Report: YouTube Community Guidelines Enforcement (www.google.com, 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "124", "geographical": "Global"}, "description": "The chart shows the percentage of videos removed by YouTube for the period October 2017-March 2022, by first source of detection (automated flagging or human detection). Flags from human detection can come from a user or a member of YouTube’s Trusted Flagger program. Trusted Flagger program members include individuals, NGOs, and government agencies that are particularly effective at notifying YouTube of content that violates their Community Guidelines. The chart shows that automated flagging is by far the first source of detection compared to human detection. "},
{"data": [{"data": [72.2, 69.2, 60.2, 66.7, 54.4, 52, 37, 33.5, 25.5, 15.5, 7.9, 14.1, 9.8, 9.1, 9.5], "name": "Spam, misleading and scams"}, {"data": [10.2, 13.9, 20.5, 14.8, 15.5, 15.8, 24.3, 28.3, 31.7, 41, 53.6, 29.9, 31.9, 31.5, 24.9], "name": "Child safety"}, {"data": [9.9, 9.5, 9.7, 8.4, 14.4, 14.1, 14.3, 14.6, 20, 15.8, 16.5, 22.4, 18.4, 18.5, 16.9], "name": "Nudity or sexual"}, {"data": [3.4, 2.9, 4.3, 5.2, 9.7, 9.8, 11.4, 10.6, 14.2, 20.6, 15.6, 16.8, 23.3, 20, 21.2], "name": "Violent or graphic"}, {"data": [2.2, 1.7, 2.6, 1.3, 1.9, 3.1, 5, 1.8, 2.5, 2.8, 2.2, 4.8, 4.5, 8.1, 12.4], "name": "Harmful or dangerous"}, {"data": [0.8, 1.5, 1.1, 1.2, 1.7, 1.7, 1.4, 1.5, 1.9, 1.7, 0.8, 0.1, 1.7, null, 0.2], "name": "Other"}, {"data": [0.7, 0.6, 0.8, 1.1, 1, 1.5, 1.8, 0.7, 1.1, 1, 0.9, 1.4, 1.8, 2.4, 2.5], "name": "Hateful or abusive"}, {"data": [0.4, 0.5, 0.6, 0.8, 0.9, 1.4, 4.2, 8.1, 2.5, 0.8, 0.9, 6.9, 4, 1.9, 1.6], "name": "Promotion of violence or violent extremism"}, {"data": [0.2, 0.2, 0.2, 0.4, 0.5, 0.6, 0.6, 0.9, 0.6, 0.8, 1.6, 3.7, 4.6, 8.6, 10.9], "name": "Harassement and cyberbullying"}], "_data": [["Period", "Spam, misleading and scams", "Child safety", "Nudity or sexual", "Violent or graphic", "Harmful or dangerous", "Other", "Hateful or abusive", "Promotion of violence or violent extremism", "Harassement and cyberbullying"], ["September 2018", "72.2", "10.2", "9.9", "3.4", "2.2", "0.8", "0.7", "0.4", "0.2"], ["October-December 2018", "69.2", "13.9", "9.5", "2.9", "1.7", "1.5", "0.6", "0.5", "0.2"], ["January-March 2019", "60.2", "20.5", "9.7", "4.3", "2.6", "1.1", "0.8", "0.6", "0.2"], ["April-June 2019", "66.7", "14.8", "8.4", "5.2", "1.3", "1.2", "1.1", "0.8", "0.4"], ["July-September 2019", "54.4", "15.5", "14.4", "9.7", "1.9", "1.7", "1", "0.9", "0.5"], ["October-December 2019", "52", "15.8", "14.1", "9.8", "3.1", "1.7", "1.5", "1.4", "0.6"], ["January-March 2020", "37", "24.3", "14.3", "11.4", "5", "1.4", "1.8", "4.2", "0.6"], ["April-June 2020", "33.5", "28.3", "14.6", "10.6", "1.8", "1.5", "0.7", "8.1", "0.9"], ["July-September 2020", "25.5", "31.7", "20", "14.2", "2.5", "1.9", "1.1", "2.5", "0.6"], ["October-December 2020", "15.5", "41", "15.8", "20.6", "2.8", "1.7", "1", "0.8", "0.8"], ["January-March 2021", "7.9", "53.6", "16.5", "15.6", "2.2", "0.8", "0.9", "0.9", "1.6"], ["April-June 2021", "14.1", "29.9", "22.4", "16.8", "4.8", "0.1", "1.4", "6.9", "3.7"], ["July-September 2021", "9.8", "31.9", "18.4", "23.3", "4.5", "1.7", "1.8", "4", "4.6"], ["October-December 2021", "9.1", "31.5", "18.5", "20.0", "8.1", "", "2.4", "1.9", "8.6"], ["January-March 2022", "9.5", "24.9", "16.9", "21.2", "12.4", "0.2", "2.5", "1.6", "10.9"]], "labels": {"name": "Period", "values": ["September 2018", "October-December 2018", "January-March 2019", "April-June 2019", "July-September 2019", "October-December 2019", "January-March 2020", "April-June 2020", "July-September 2020", "October-December 2020", "January-March 2021", "April-June 2021", "July-September 2021", "October-December 2021", "January-March 2022"]}, "metadata": {"link": "https://transparencyreport.google.com/youtube-policy/removals?hl=en", "type": "Problem", "unit": "Per Cent(%)", "year": "2018-2021", "title": "Videos Removed by YouTube, by Removal Reason", "topic": "Illegal Content", "method": "Self-reporting", "source": "Google. Transparency Report: YouTube Community Guidelines Enforcement (www.google.com, 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "125", "geographical": "Global"}, "description": "This chart shows the distribution of videos removed by YouTube, by the reason removal, over the period September 2018-March 2022. These removal reasons correspond to YouTube’s Community Guidelines. Reviewers evaluate flagged videos against all of YouTube's Community Guidelines and policies, regardless of the reason the video was originally flagged. As the chart shows, the most frequent reasons of removal of videos are child abusive content, violent or graphic content and nudity or sexual content. In the first quarter of 2022, the child safety content decline by 53.5% compared to the same period of 2021, while harmful or dangerous content increased in the same period by 463% and harassement and cyberbullying by 579%."},
{"data": [{"data": [12468976, 4614456, 181430, 755], "name": "Number of videos removed"}], "_data": [["Human detection", "Number of videos removed"], ["User", "12468976"], ["Individual trusted flagger", "4614456"], ["Non-governmental organisations", "181430"], ["Government agencies", "755"]], "labels": {"name": "Human detection", "values": ["User", "Individual trusted flagger", "Non-governmental organisations", "Government agencies"]}, "metadata": {"link": "https://transparencyreport.google.com/youtube-policy/removals?hl=en", "type": "Problem", "unit": "Number of videos removed", "year": "2017-2022", "title": "Videos Removed by YouTube, by Source of First Detection (Human)", "topic": "Illegal Content", "method": "Self-reporting", "source": "Google. Transparency Report: YouTube Community Guidelines Enforcement (www.google.com, 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "126", "geographical": "Global"}, "description": "The chart shows the number of videos removed by YouTube for the period October 2017-March 2022, by first source of detection (human detection). Flags from human detection can come from a user or a member of YouTube’s Trusted Flagger program. Trusted Flagger program members include individuals, NGOs, and government agencies that are particularly effective at notifying YouTube of content that violates their Community Guidelines. The chart shows that the highest number of removed videos were first noticed by users (12,468,976 videos), followed by individual trusted flaggers (4,614,456 videos), NGOs (181,430 videos) and government agencies (755 videos)."},
{"data": [{"data": [99.8, 99.7, 99.7, 99.7, 99.9, 99.9, 99.9, 99.9, 99.9, 99.8, 99.7, 99.9, 99.8, 99.8, 99.7, 99.6, 99.6, 99.7], "name": "Percentage of content actioned that Facebook found and flagged before users reported it"}, {"data": [0.2, 0.3, 0.3, 0.3, 0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.3, 0.1, 0.2, 0.2, 0.3, 0.4, 0.4, 0.3], "name": "Percentage of content actioned that users reported first"}], "_data": [["Period", "Percentage of content actioned that Facebook found and flagged before users reported it", "Percentage of content actioned that users reported first"], ["October-December 2017", "99.8", "0.2"], ["January-March 2018", "99.7", "0.3"], ["April-June 2018", "99.7", "0.3"], ["July-September 2018", "99.7", "0.3"], ["October-December 2018", "99.9", "0.1"], ["January-March 2019", "99.9", "0.1"], ["April-June 2019", "99.9", "0.1"], ["July-September 2019", "99.9", "0.1"], ["October-December 2019", "99.9", "0.1"], ["January-March 2020", "99.8", "0.2"], ["April-June 2020", "99.7", "0.3"], ["July-September 2020", "99.9", "0.1"], ["October-December 2020", "99.8", "0.2"], ["January-March 2021", "99.8", "0.2"], ["April-June 2021", "99.7", "0.3"], ["July-September 2021", "99.6", "0.4"], ["October-December 2021", "99.6", "0.4"], ["January-March 2022", "99.7", "0.3"]], "labels": {"name": "Period", "values": ["October-December 2017", "January-March 2018", "April-June 2018", "July-September 2018", "October-December 2018", "January-March 2019", "April-June 2019", "July-September 2019", "October-December 2019", "January-March 2020", "April-June 2020", "July-September 2020", "October-December 2020", "January-March 2021", "April-June 2021", "July-September 2021", "October-December 2021", "January-March 2022"]}, "metadata": {"link": "https://transparency.fb.com/data/community-standards-enforcement/spam/facebook/", "type": "Problem", "unit": "Per Cent (%)", "year": "2017-2022", "title": "Percentage of Content Found by Facebook as Containing Spam Compared to the Content Reported by the Users", "topic": "Illegal Content", "method": "Self-reporting", "source": "Meta. Transparency Report: Spam (June 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "127", "geographical": "Global"}, "description": "This chart shows the percentage of content found by Facebook as containing spam compared to the content reported by the users over the period October 2017 - March 2022. As the results show, almost all of the content containing spam was first found by Facebook (the percentage remains above 99.5% for the whole period)."},
{"data": [{"data": [94.4, 95.8, 96.9, 97.3, 98.2, 98.2, 98.6, 98.8, 99, 99.1, 97.7, 98.2, 97.9, 98.6, 98.9, 98.8, 97.7, 96.7], "name": "Percentage of content actioned that Facebook found and flagged before users reported it"}, {"data": [5.6, 4.2, 3.1, 2.7, 1.8, 1.8, 1.4, 1.2, 1, 0.9, 2.3, 1.8, 2.1, 1.4, 1.1, 1.2, 2.3, 3.3], "name": "Percentage of content actioned that users reported first"}], "_data": [["Period", "Percentage of content actioned that Facebook found and flagged before users reported it", "Percentage of content actioned that users reported first"], ["October-December 2017", "94.4", "5.6"], ["January-March 2018", "95.8", "4.2"], ["April-June 2018", "96.9", "3.1"], ["July-September 2018", "97.3", "2.7"], ["October-December 2018", "98.2", "1.8"], ["January-March 2019", "98.2", "1.8"], ["April-June 2019", "98.6", "1.4"], ["July-September 2019", "98.8", "1.2"], ["October-December 2019", "99", "1"], ["January-March 2020", "99.1", "0.9"], ["April-June 2020", "97.7", "2.3"], ["July-September 2020", "98.2", "1.8"], ["October-December 2020", "97.9", "2.1"], ["January-March 2021", "98.6", "1.4"], ["April-June 2021", "98.9", "1.1"], ["July-September 2021", "98.8", "1.2"], ["October-December 2021", "97.7", "2.3"], ["January-March 2022", "96.7", "3.3"]], "labels": {"name": "Period", "values": ["October-December 2017", "January-March 2018", "April-June 2018", "July-September 2018", "October-December 2018", "January-March 2019", "April-June 2019", "July-September 2019", "October-December 2019", "January-March 2020", "April-June 2020", "July-September 2020", "October-December 2020", "January-March 2021", "April-June 2021", "July-September 2021", "October-December 2021", "January-March 2022"]}, "metadata": {"link": "https://transparency.fb.com/data/community-standards-enforcement/adult-nudity-and-sexual-activity/facebook/", "type": "Problem", "unit": "Per Cent (%)", "year": "2017-2022", "title": "Percentage of Content Found by Facebook as Containing Adult Nudity and Sexual Activity Compared to the Content Reported by the Users", "topic": "Illegal Content", "method": "Self-reporting", "source": "Meta. Transparency Report: Adult Nudity and Sexual Activity (June 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "128", "geographical": "Global"}, "description": "This chart shows the percentage of content found by Facebook as containing adult nudity and sexual activity compared to the content reported by the users. As the result shows, the percentage of content actioned that Facebook found and flagged before users reported it is significantly higher that the one reported by users. "},
{"data": [{"data": [21.2, 25.5, 18.3, 12.6, 8, 9.5, 4.9], "name": "Percentage"}], "_data": [["Type of content", "Percentage"], ["Sexual", "21.2"], ["Spam, misleading and scams", "25.5"], ["Hateful or abusive", "18.3"], ["Violent or repulsive", "12.6"], ["Harmful dangerous act", "8.0"], ["Child abuse", "9.5"], ["Promotes terrorism ", "4.9"]], "labels": {"name": "Type of content", "values": ["Sexual", "Spam, misleading and scams", "Hateful or abusive", "Violent or repulsive", "Harmful dangerous act", "Child abuse", "Promotes terrorism "]}, "metadata": {"link": "https://transparencyreport.google.com/youtube-policy/flags?hl=en&request_examples=year:;flagging_reason:;flagger_type:1&lu=request_examples", "type": "Problem", "unit": "Per Cent(%)", "year": "2017-2022", "title": "Human Detection of Illegal Content Online, by Flagging Reason", "topic": "Illegal Content", "method": "Self-reporting", "source": "Google. Transparency Report: Flags - Illegal Content Online (www.google.com, 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "129", "geographical": "Global"}, "description": "The chart shows the distribution of the videos removed by Youtube based on human detection, by flagging reason. The data represents average shares of videos removed for the period October 2017-March 2022 and are calculated based on the trimestrial values included in the transparency report. The results show that the users' main flagging reason of videos is the spam, mislinding and scam content, followed by sexual content and hateful or abusive content. When flagging a video, human flaggers can select a reason they are reporting the video and leave comments or video timestamps for YouTube's reviewers. This chart shows the flagging reasons that people selected when reporting YouTube content. A single video may be flagged multiple times and may be flagged for different reasons. Reviewers evaluate flagged videos against all of the Community Guidelines and policies, regardless of why they were originally flagged. Flagging a video does not necessarily result in it being removed. Human flagged videos are removed for violations of Community Guidelines once a trained reviewer confirms a policy violation."},
{"data": [{"data": [193, 142, 205, 129], "name": "Number of attacks"}], "_data": [["Year", "Number of attacks"], ["2015", "193"], ["2016", "142"], ["2017", "205"], ["2018", "129"]], "labels": {"name": "Year", "values": ["2015", "2016", "2017", "2018"]}, "metadata": {"link": "https://www.europol.europa.eu/activities-services/main-reports/terrorism-situation-and-trend-report-2019-te-sat", "type": "Problem", "unit": "Number of attacks", "year": "2015-2018", "title": "Number of Failed, Foiled or Completed Attacks ", "topic": "Incitement to Terrorism", "method": "Data mining", "source": "European Union Agency for Law Enforcement Cooperation. European Union Terrorism Situation and Trend Report (The Hague: Europol, 2019)", "sub_topic": "Number of terrorist attacks", "chart_number": "130", "geographical": "European Union"}, "description": "The total number of attacks (129) decreased after a sharp spike in 2017 (205), primarily because of the decrease in reported separatist-related incidents."},
{"data": [{"data": [67, 16, 12, 3, 2], "name": "Number of attacks"}], "_data": [["Names", "Number of attacks"], ["Separatist", "67"], ["Jihadist", "16"], ["Left-wing", "12"], ["Right-wing", "3"], ["N/S", "2"]], "labels": {"name": "Names", "values": ["Separatist", "Jihadist", "Left-wing", "Right-wing", "N/S"]}, "metadata": {"link": "https://www.europol.europa.eu/sites/default/files/documents/tesat_2018_1.pdf", "type": "Problem", "year": "2017", "title": "Failed, Foiled or Completed Attacks by Affiliation (2017)", "topic": "Incitement to Terrorism", "method": "Data mining", "source": "European Union Agency for Law Enforcement Cooperation. European Union Terrorism Situation and Trend Report (The Hague: Europol, 2018)", "sub_topic": "Number of terrorist attacks", "chart_number": "130", "geographical": "European Union"}, "description": "In 2017, the attacks specifically classified as ethno-nationalist and separatist accounted for the largest proportion of failed, foiled and completed terrorist attacks."},
{"data": [{"data": [28.3, 19.1, 48.5, null, null, null], "name": "First monitoring (December 2016)"}, {"data": [66.5, 37.4, 66, null, null, null], "name": "Second monitoring (May 2017)"}, {"data": [79.8, 45.7, 75, null, null, null], "name": "Third monitoring (December 2017)"}, {"data": [82.4, 43.5, 85.4, 70.6, null, null], "name": "Fourth monitoring (December 2018)"}, {"data": [87.6, 35.9, 79.7, 42, 100, null], "name": "Fifth monitoring (December 2019)"}, {"data": [70.2, 49.8, 58.8, 66.2, 100, 80.1], "name": "Sixth Monitoring (April 2021)"}], "_data": [["Channel", "First monitoring (December 2016)", "Second monitoring (May 2017)", "Third monitoring (December 2017)", "Fourth monitoring (December 2018)", "Fifth monitoring (December 2019)", "Sixth Monitoring (April 2021)"], ["Facebook", "28.3", "66.5", "79.8", "82.4", "87.6", "70.2"], ["Twitter", "19.1", "37.4", "45.7", "43.5", "35.9", "49.8"], ["YouTube", "48.5", "66", "75", "85.4", "79.7", "58.8"], ["Instagram", "", "", "", "70.6", "42", "66.2"], ["Jeuxvideo.com", "", "", "", "", "100", "100"], ["TikTok", "", "", "", "", "", "80.1"]], "labels": {"name": "Channel", "values": ["Facebook", "Twitter", "YouTube", "Instagram", "Jeuxvideo.com", "TikTok"]}, "metadata": {"link": "https://ec.europa.eu/info/sites/default/files/factsheet-6th-monitoring-round-of-the-code-of-conduct_october2021_en_1.pdf", "type": "Solution", "unit": "Per Cent (%)", "year": "2016-2021", "Range": "0 100", "title": "Rate of Hate Speech Content Removal Across ICT Companies", "topic": "Hate Speech", "method": "Self-reporting", "source": "European Commission. Sixth Evaluation on the Code of Conduct on Countering Illegal Hate Speech Online (Brussels: European Commission, 2021)  ", "sub_topic": "Removal of hate speech", "chart_number": "132", "geographical": "European Union"}, "description": "The chart presents the distribution of hate speech content removal by the ICT companies, based on data reported by social media platforms participating in the European Commission's Code of conduct. The sixth monitoring exercise shows that out of the platforms participating in the Code of conduct, Jeuxvideo.com has the highest rate of removal, followed by TikTok and Facebook. Twitter continues to have the lowest rate of removal, Instagram significantly increased its removal rate (compared to the previous monitoring period), and Youtube's removal rate continue to decline. Overall, the sixth monitoring exercise shows that the Code of conduct continues to bring positive results when it comes to illegal hate speech removal across social medial platforms."},
{"data": [{"data": [11.4, 6.9, null, null, null, null, 52, 3.4, null, null, null, null, 49.5, null, null, 3.6, null, null, 14.3, null, null, null, null, null, null, 20.5], "name": "December 2016"}, {"data": [76.1, 51.2, null, 33.6, 84.8, 46.5, 80.1, 38.9, 81.8, null, 17.2, null, 82, 94.5, 20, 81.7, 63.3, 90.9, 29.5, 60.3, 21, 53.3, null, 56.8, 71.4, 39.3], "name": "May 2017"}, {"data": [74, 83, 91.3, 37.1, 73.8, 54.5, 100, 42.5, 89.8, null, 73.8, 70.6, 75, 92.6, 69.6, 66.9, 69, 99, 50, 78.4, 38.6, 76.2, 67.3, 78.3, 67.7, 66.3], "name": "December 2017"}, {"data": [66.7, 78.9, 100, 73.4, 100, 62.5, 87.6, null, 95.8, 100, 59.7, 39.1, 71.8, 89.2, 73.5, 56.6, 66.9, 94.9, 75.8, 69.7, 35.7, 92.2, 81.3, 91.7, 85.8, 54.3], "name": "December 2018"}, {"data": [73.3, null, 100, 83.2, 94.1, 57.9, 76.6, null, 92, null, 46.9, 12.1, 87.8, 95, null, 46.2, 74.7, 94.1, null, 56.8, 23.2, 96.5, 47.9, 54.8, 94.3, 42.5], "name": "December 2019"}, {"data": [86.8, 73.5, 45.2, 66.3, null, 37.4, 95.9, null, 90.6, 60.6, 68.2, null, 81.5, 36.1, 32.3, 52, 68.4, 95.4, 47.9, 34.5, 31.2, 100, 35.4, null, 46.5, null], "name": "April 2021"}], "_data": [["Country", "December 2016", "May 2017", "December 2017", "December 2018", "December 2019", "April 2021"], ["Austria", "11.4", "76.1", "74", "66.7", "73.3", "86.8"], ["Belgium", "6.9", "51.2", "83", "78.9", "", "73.5"], ["Bulgaria", "", "", "91.3", "100", "100", "45.2"], ["Croatia", "", "33.6", "37.1", "73.4", "83.2", "66.3"], ["Cyprus", "", "84.8", "73.8", "100", "94.1"], ["Czech Republic", "", "46.5", "54.5", "62.5", "57.9", "37.4"], ["Germany", "52", "80.1", "100", "87.6", "76.6", "95.9"], ["Denmark", "3.4", "38.9", "42.5"], ["Estonia", "", "81.8", "89.8", "95.8", "92", "90.6"], ["Greece", "", "", "", "100", "", "60.6"], ["Spain", "", "17.2", "73.8", "59.7", "46.9", "68.2"], ["Finland", "", "", "70.6", "39.1", "12.1"], ["France", "49.5", "82", "75", "71.8", "87.8", "81.5"], ["Hungary", "", "94.5", "92.6", "89.2", "95", "36.1"], ["Ireland", "", "20", "69.6", "73.5", "", "32.3"], ["Italy", "3.6", "81.7", "66.9", "56.6", "46.2", "52"], ["Latvia", "", "63.3", "69", "66.9", "74.7", "68.4"], ["Lithuania", "", "90.9", "99", "94.9", "94.1", "95.4"], ["Netherlands", "14.3", "29.5", "50", "75.8", "", "47.9"], ["Poland", "", "60.3", "78.4", "69.7", "56.8", "34.5"], ["Portugal", "", "21", "38.6", "35.7", "23.2", "31.2"], ["Romania", "", "53.3", "76.2", "92.2", "96.5", "100"], ["Sweden", "", "", "67.3", "81.3", "47.9", "35.4"], ["Slovenia", "0", "56.8", "78.3", "91.7", "54.8"], ["Slovakia", "0", "71.4", "67.7", "85.8", "94.3", "46.5"], ["United Kingdom", "20.5", "39.3", "66.3", "54.3", "42.5"]], "labels": {"name": "Country", "values": ["Austria", "Belgium", "Bulgaria", "Croatia", "Cyprus", "Czech Republic", "Germany", "Denmark", "Estonia", "Greece", "Spain", "Finland", "France", "Hungary", "Ireland", "Italy", "Latvia", "Lithuania", "Netherlands", "Poland", "Portugal", "Romania", "Sweden", "Slovenia", "Slovakia", "United Kingdom"]}, "metadata": {"link": "https://ec.europa.eu/info/sites/default/files/factsheet-6th-monitoring-round-of-the-code-of-conduct_october2021_en_1.pdf", "type": "Solution", "unit": "Rate of removals (%)", "year": "2016-2021", "Range": "0 100", "title": "Rate of Posts’ Removals by Social Media Platforms Across European Union Countries", "topic": "Hate Speech", "method": "Self-reporting", "source": "European Commission. Sixth Evaluation on the Code of Conduct on Countering Illegal Hate Speech Online (Brussels: European Commission, 2021)  ", "sub_topic": "Removal of hate speech", "chart_number": "133", "geographical": "European Union"}, "description": "The chart shows the per cent of reviewed posts which social media platforms removed in each of six monitoring periods, by European Union member state. The results are based on data reported by social media platforms participating in the European Commission's Code of conduct. Removal rates ranged from as high as 100% to as low as 0%. The United Kingdom left the European Union on 31 January 2020. In 2021, three organisations from the United Kingdom took part to the monitoring exercise, with an overall average removal rate of 43%. <br/> Notes: The data from Belgium, Greece, Ireland (2019) and Malta (2021) is not included in the chart due to the too low number of notifications made to companies (<20). In 2019, Malta, Luxembourg, the Netherlands and Denmark organisations did not submit cases for the exercise, while same applies for Slovenia, Cyprus, Finland, Luxembourg, and Denmark organisations in 2021."},
{"data": [{"data": [91.2, 26.4, 5.6, 61, 22.5], "name": "General user"}, {"data": [95.8, 72.7, 32.7, 77.8, null], "name": "Trusted flagger/Reporter"}], "_data": [["Online Platform", "General user", "Trusted flagger/Reporter"], ["Facebook", "91.2", "95.8"], ["Twitter", "26.4", "72.7"], ["YouTube", "5.6", "32.7"], ["Instagram", "61", "77.8"], ["Jeuxvideo.com", "22.5", "0"]], "labels": {"name": "Online Platform", "values": ["Facebook", "Twitter", "YouTube", "Instagram", "Jeuxvideo.com"]}, "metadata": {"link": "https://ec.europa.eu/info/sites/info/files/codeofconduct_2020_factsheet_12.pdf", "type": "Solution", "unit": "Per Cent (%)", "year": "2020", "title": "Feedback Provided by Online Platforms to Different Types of User (2019)", "topic": "Hate Speech", "method": "Self-reporting", "source": "European Commission. Fifth Evaluation on the Code of Conduct on Countering Illegal Hate Speech Online (Brussels: European Commission, 2020)  ", "sub_topic": "Removal of hate speech", "chart_number": "134", "geographical": "European Union"}, "description": "This chart shows the per cent of feedback provided by online platforms to different types of users (general user or trusted flagger/reporter). The results are based on data reported by social media platforms participating in the European Commission's Code of conduct. The data shows that platforms have higher rates of providing feedback to trusted flaggers compared to the ones to the general users, with differences varying between 4.6% (Facebook) and 46.3% (Twitter). One of the European Commission's conclusions included in the fifth monitoring exercice is that online platforms must improve their feedback to users'notifications. "},
{"data": [{"data": [33.1, 3.8, 15, 4.6, 3.8, 7.1, 9.4, 3.6, 4.9, 9.9, 1.1, 3.7], "name": "Per cent (%)"}], "_data": [["Type", "Per cent (%)"], ["Sexual orientation", "33.1"], ["Race", "3.8"], ["Xenophobia (including anti-migrant hatred)", "15"], ["Ethnic origin", "4.6"], ["National origin", "3.8"], ["Antisemitism", "7.1"], ["Anti-Muslim hatred", "9.4"], ["Other", "3.6"], ["Afrophobia", "4.9"], ["Anti-gypsyism", "9.9"], ["Religion", "1.1"], ["Gender-based hate speech", "3.7"]], "labels": {"name": "Type", "values": ["Sexual orientation", "Race", "Xenophobia (including anti-migrant hatred)", "Ethnic origin", "National origin", "Antisemitism", "Anti-Muslim hatred", "Other", "Afrophobia", "Anti-gypsyism", "Religion", "Gender-based hate speech"]}, "metadata": {"link": "https://ec.europa.eu/info/sites/info/files/codeofconduct_2020_factsheet_12.pdf", "type": "Solution", "year": "2020", "title": "Grounds of Hatred Reported by Social Media Platforms (2019)", "topic": "Hate Speech", "method": "Self-reporting", "source": "European Commission. Fifth Evaluation on the Code of Conduct on Countering Illegal Hate Speech Online (Brussels: European Commission, 2020)  ", "sub_topic": "Removal of hate speech", "chart_number": "135", "geographical": "European Union"}, "description": "The chart shows the grounds of hatred reported for reviewed posts, based on data reported by social media platforms participating in the European Commission's Code of conduct. Sexual orientation and xenophobia were the most common grounds for hatred, while religion, race and national origin were the least common grounds for hatred."},
{"data": [{"data": [24, 16.6, 15.1, 11.2, 9.3, 6, 6, 3.5, 2, 1, 1, 1, 0.8, 0.8, 0.7, 0.5, 0.3, 0.15, 0.15, 0.13, 0.12, 0.11, 0.1, 0.09], "name": "The economy of origin of right holder whose intelllectual property rights are infringed"}], "_data": [["Country", "The economy of origin of right holder whose intelllectual property rights are infringed"], ["United States", "24"], ["France", "16.6"], ["Italy", "15.1"], ["Switzerland", "11.2"], ["Germany", "9.3"], ["Japan", "6"], ["Multiple owners", "6"], ["Korea", "3.5"], ["United Kingdom", "2"], ["Spain", "1"], ["Luxembourg", "1"], ["Finland", "1"], ["Sweden", "0.8"], ["Canada", "0.8"], ["Denmark", "0.7"], ["Belgium", "0.5"], ["Brazil", "0.3"], ["Netherlands", "0.15"], ["Austria", "0.15"], ["Hong Kong (China)", "0.13"], ["Hungary", "0.12"], ["Singapore", "0.11"], ["Australia", "0.1"], ["China (People's Republic of)", "0.09"]], "labels": {"name": "Country", "values": ["United States", "France", "Italy", "Switzerland", "Germany", "Japan", "Multiple owners", "Korea", "United Kingdom", "Spain", "Luxembourg", "Finland", "Sweden", "Canada", "Denmark", "Belgium", "Brazil", "Netherlands", "Austria", "Hong Kong (China)", "Hungary", "Singapore", "Australia", "China (People's Republic of)"]}, "metadata": {"link": "https://euipo.europa.eu/ohimportal/en/web/observatory/trends-in-trade-in-counterfeit-and-pirated-goods", "type": "Problem", "unit": "Percent (%)", "year": "2016", "title": "Seizures of Counterfeit And Pirated Goods: Top Economies of Origin of Right Holders Whose Intellectual Property Rights Were Infringed", "topic": "Copyright Infringement", "method": "Survey", "source": "Organisation for Economic Co-operation and Development and European Union Intellectual Property Office.Trends in Trade in Counterfeit and Pirated Goods: Mapping the Economic Impact (Paris: OECD, 2016)", "sub_topic": "Prevalence of copyright infringement", "chart_number": "136", "geographical": "Global"}, "description": "This chart looks at economies in which the right holders whose intellectual property rights are infringed are located (2014-2016). Location refers to the place where the headquarters of a right holder is registered. Almost 24% of the total value of seized products refers to intellectual property rights of holders registered in the United States, followed by France (16.6%), Italy (15.1%), Switzerland (11.2%) and Germany (9.3%). The data are presented with approximation. For more details please visit the source."},
{"data": [{"data": [2200680, 2120589], "name": "Number of web addresses"}], "_data": [["Value", "Number of web addresses"], ["Total web addresses not delisted", "2200680"], ["Total web addresses delisted", "2120589"]], "labels": {"name": "Value", "values": ["Total web addresses not delisted", "Total web addresses delisted"]}, "metadata": {"link": "https://transparencyreport.google.com/eu-privacy/overview?hl=en", "type": "Solution", "year": "2014-2022", "title": "Share of Web Addresses Requested to Be Delisted", "topic": "Illegal Content", "method": "Self-reporting", "source": "Google. Transparency Report: Requests to Delist Content Under European Privacy Law (www.google.com, 2022)", "sub_topic": "Removal of illegal content", "chart_number": "137", "geographical": "Global"}, "description": "The chart shows the percentage of web addresses that have been delisted after review out of total requests received. The data cover the period 28 May 2014 to 13 June 2022. Web addresses delisting requests that are still pending review, or that require additional information in order to process, are not included in the graph. The last access date of the live chart is 13 June 2022."},
{"data": [{"data": [52, 59, 59, 60, 67, 49, 54, 63, 54, 62, 72, 67, 71, 68, 64], "name": "Number of countries with declined scores"}, {"data": [83, 56, 43, 38, 34, 34, 37, 43, 40, 33, 43, 36, 35, 50, 37], "name": "Number of countries with improved score"}], "_data": [["Year", "Number of countries with declined scores", "Number of countries with improved score"], ["2005", "52", "83"], ["2006", "59", "56"], ["2007", "59", "43"], ["2008", "60", "38"], ["2009", "67", "34"], ["2010", "49", "34"], ["2011", "54", "37"], ["2012", "63", "43"], ["2013", "54", "40"], ["2014", "62", "33"], ["2015", "72", "43"], ["2016", "67", "36"], ["2017", "71", "35"], ["2018", "68", "50"], ["2019", "64", "37"]], "labels": {"name": "Year", "values": ["2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019"]}, "metadata": {"link": "https://freedomhouse.org/report/freedom-world/2020/leaderless-struggle-democracy", "type": "Problem", "unit": "Number countries with improved /declined score", "year": "2019", "title": "Fourteen Years of Democratic Decline", "topic": "Illegal Content", "method": "Data mining", "source": "Repucci, Sarah. \"Democracy and Pluralism Are Under Assault,\" Freedom House, 2020", "sub_topic": "Prevalence of illegal content", "chart_number": "138", "geographical": "Global"}, "description": "The chart shows the evolution of the countries' Freedom of the World score for the past 15 years, based on a report from Freedom House. The results show that the global freedom has declined constantly in the last the 14 years. The gap between setbacks and gains widened compared with 2018, as individuals in 64 countries experienced deterioration in their political rights and civil liberties while those in just 37 experienced improvements. The negative pattern affected all regime types, but the impact was most visible near the top and the bottom of the scale."},
{"data": [{"data": [642.8, 577.2, 510.5, 504.2, 288.5, 259.2], "name": "Requests"}], "_data": [["Organisation", "Requests"], ["Rivendell", "642.8"], ["BPI (British Recorded Music Industry) Ltd", "577.2"], ["Remove your Media LLC", "510.5"], ["MG Premium Ltd", "504.2"], ["APDIF Mexico", "288.5"], ["APDIF do Brasil", "259.2"]], "labels": {"name": "Organisation", "values": ["Rivendell", "BPI (British Recorded Music Industry) Ltd", "Remove your Media LLC", "MG Premium Ltd", "APDIF Mexico", "APDIF do Brasil"]}, "metadata": {"link": "https://transparencyreport.google.com/copyright/overview?hl=en", "type": "Solution", "unit": "Million", "year": "2022", "title": "Reporting Organisations or Copyright Owners Who Have Submitted or Been Cited in the Most Requests", "topic": "Copyright Infringement", "method": "Data mining", "source": "Google. Transparency Report: Content Delistings Due To Copyright (www.google.com, 2022)", "sub_topic": "Response to alleged copyright infringement", "chart_number": "139", "geographical": "Global"}, "description": "The chart shows the reporting organisations or copyright owners who have submitted or been cited in the most requests. The date of the extraction of the current values from the live chart of Google is 13 June 2022."},
{"data": [{"data": [14, 8], "name": "Film"}, {"data": [8, 11], "name": "Music"}, {"data": [34, 25], "name": "Television"}], "_data": [["Type of content", "Film", "Music", "Television"], ["Desktop", "14", "8", "34"], ["Mobile ", "8", "11", "25"]], "labels": {"name": "Type of content", "values": ["Desktop", "Mobile "]}, "metadata": {"link": "https://op.europa.eu/s/n2Du", "type": "Problem", "unit": "Percent (%)", "year": "2018", "title": "Access to Pirated Content in European Union by Content Type and Device (2018)", "topic": "Copyright Infringement", "method": "Data mining", "source": "European Union Intellectual Property Office. Online Copyright Infringement in the European Union: Music, Films and TV (2017-2018), Trends and Drivers (Alicante: European Union Intellectual Property Office, 2019)", "sub_topic": "Prevalence of pirated content", "chart_number": "140", "geographical": "European Union"}, "description": "This graph shows the distribution of online infringement in European Union across the three content types and the desktop/mobile dimension for the nine months of 2018 covered by the data. Television copyright infringement represented nearly 60% of the total, followed by film and music piracy. The use of desktop devices to access television content and films is greater than that of mobile devices, while access to music is greater from mobile devices. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [19.51, 19.39, 9.35, 12.8, 10.19, 12.37, 8.72, 9.9, 8.8, 7.38, 5.35, 8.55, 5.74, 7.48, 6.19, 7.35, 7, 8.01, 6.65, 6.19, 6.95, 6.32, 4.21, 5.67, 5.24, 3.98, 5.09, 3.59], "name": "Television"}, {"data": [5.11, 5.06, 5.97, 2.08, 2.87, 2.61, 1.98, 2.07, 2.33, 2.03, 2.18, 1.34, 1.92, 2.48, 2.13, 1.79, 1.54, 1.26, 1.83, 2.23, 0.9, 1.3, 1.92, 0.77, 0.7, 1.28, 0.96, 0.49], "name": "Music"}, {"data": [2.37, 1.89, 3.4, 3.03, 3.98, 1.73, 3.87, 1.66, 2.21, 3.38, 4.86, 2.27, 4.42, 1.95, 3.54, 2.53, 2.74, 1.82, 1.6, 1.22, 1.31, 1.51, 2.59, 1.1, 1.04, 1.72, 0.79, 0.48], "name": "Film"}], "_data": [["Country", "Television", "Music", "Film"], ["Latvia", "19.51", "5.11", "2.37"], ["Lithuania", "19.39", "5.06", "1.89"], ["Bulgaria", "9.35", "5.97", "3.40"], ["Malta", "12.80", "2.08", "3.03"], ["Slovakia", "10.19", "2.87", "3.98"], ["Estonia", "12.37", "2.61", "1.73"], ["Cyprus", "8.72", "1.98", "3.87"], ["Portugal", "9.90", "2.07", "1.66"], ["Czech Republic", "8.80", "2.33", "2.21"], ["Croatia", "7.38", "2.03", "3.38"], ["Greece", "5.35", "2.18", "4.86"], ["Ireland", "8.55", "1.34", "2.27"], ["Poland", "5.74", "1.92", "4.42"], ["Hungary", "7.48", "2.48", "1.95"], ["Romania", "6.19", "2.13", "3.54"], ["Belgium", "7.35", "1.79", "2.53"], ["France", "7.00", "1.54", "2.74"], ["Luxembourg", "8.01", "1.26", "1.82"], ["Netherlands", "6.65", "1.83", "1.60"], ["Slovenia", "6.19", "2.23", "1.22"], ["Sweden", "6.95", "0.90", "1.31"], ["United Kingdom", "6.32", "1.30", "1.51"], ["Spain", "4.21", "1.92", "2.59"], ["Austria", "5.67", "0.77", "1.10"], ["Denmark", "5.24", "0.70", "1.04"], ["Italy", "3.98", "1.28", "1.72"], ["Germany", "5.09", "0.96", "0.79"], ["Finland", "3.59", "0.49", "0.48"]], "labels": {"name": "Country", "values": ["Latvia", "Lithuania", "Bulgaria", "Malta", "Slovakia", "Estonia", "Cyprus", "Portugal", "Czech Republic", "Croatia", "Greece", "Ireland", "Poland", "Hungary", "Romania", "Belgium", "France", "Luxembourg", "Netherlands", "Slovenia", "Sweden", "United Kingdom", "Spain", "Austria", "Denmark", "Italy", "Germany", "Finland"]}, "metadata": {"link": "https://op.europa.eu/s/n2Du", "type": "Problem", "unit": "Per internet user per month", "year": "2018", "title": "Total Piracy by European Union Member State and Content Type (2018)", "topic": "Copyright Infringement", "method": "Administrative data", "source": "European Union Intellectual Property Office. Online Copyright Infringement in the European Union: Music, Films and TV (2017-2018), Trends and Drivers (Alicante: European Union Intellectual Property Office, 2019)", "sub_topic": "Prevalence of pirated content", "chart_number": "141", "geographical": "European Union"}, "description": "This chart shows piracy by European Union member state. For each country, piracy is broken down by content type accessed. In two countries, Latvia and Lithuania, consumption of pirated content is clearly higher (more than 26 accesses per month) than in the rest of the European Union. Finland has the lowest rate at 4.6 access per user per month. Germany, Italy, Denmark, Austria, Spain, Sweden, the United Kingdom and Slovenia are also below the European Union average of 9.7. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [75.3, 8.8, 10.4, 5.5], "name": "Distribution of piracy"}], "_data": [["Type of content", "Distribution of piracy"], ["Streaming", "75.3"], ["Download", "8.8"], ["Torrent", "10.4"], ["Ripper", "5.5"]], "labels": {"name": "Type of content", "values": ["Streaming", "Download", "Torrent", "Ripper"]}, "metadata": {"link": "https://op.europa.eu/s/n2Du", "type": "Problem", "year": "2018", "title": "Total Piracy by Access Method (2018)", "topic": "Copyright Infringement", "method": "Administrative data", "source": "European Union Intellectual Property Office. Online Copyright Infringement in the European Union: Music, Films and TV (2017-2018), Trends and Drivers (Alicante: European Union Intellectual Property Office, 2019)", "sub_topic": "Prevalence of pirated content", "chart_number": "142", "geographical": "European Union"}, "description": "This doughnut chart shows the distribution of piracy in the European Union by access method. As presented, streaming is the preferred method with a 75% share. The remaining 25% is divided between download, torrent and ripper. Nearly 95% of the streaming activity is concentrated in television and film. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [9.2, 13.2, 18.8, 15.4, 13.6, 9.2, 8.4, 17.6, 13.1, 10.2, 4.8, 12.9, 14.1, 13.6, 12.9, 8.7, 27.8, 14.2, 26.5, 17.4, 11.7, 14.7, 14.2, 14.9, 11, 9.3, 17.4, 10.8, 11.5], "name": "First nine months 2017"}, {"data": [7.5, 11.7, 18.7, 14.6, 13.3, 6.8, 7, 16.7, 12.4, 8.7, 4.6, 11.3, 12.8, 11.9, 12.2, 7, 26.3, 11.1, 27, 17.9, 10.1, 12.1, 13.6, 11.9, 9.2, 9.6, 17, 9.1, 9.7], "name": "First nine months 2018"}, {"data": [-18, -11.3, -0.2, -5.7, -2.1, -25.7, -16.7, -4.8, -5.4, -14.4, -5.2, -12.8, -9.2, -12.3, -5.6, -19.8, -5.2, -21.8, 2, 2.8, -14.1, -17.8, -4.1, -20.4, -16.3, 3.9, -1.9, -15.5, -15.1], "name": "12 month trend %"}], "_data": [["Country", "First nine months 2017", "First nine months 2018", "12 month trend %"], ["Austria", "9.2", "7.5", "-18"], ["Belgium", "13.2", "11.7", "-11.3"], ["Bulgaria", "18.8", "18.7", "-0.2"], ["Cyprus", "15.4", "14.6", "-5.7"], ["Czech Republic", "13.6", "13.3", "-2.1"], ["Germany", "9.2", "6.8", "-25.7"], ["Denmark", "8.4", "7", "-16.7"], ["Estonia", "17.6", "16.7", "-4.8"], ["Greece", "13.1", "12.4", "-5.4"], ["Spain", "10.2", "8.7", "-14.4"], ["Finland", "4.8", "4.6", "-5.2"], ["France", "12.9", "11.3", "-12.8"], ["Croatia", "14.1", "12.8", "-9.2"], ["Hungary", "13.6", "11.9", "-12.3"], ["Ireland", "12.9", "12.2", "-5.6"], ["Italy", "8.7", "7", "-19.8"], ["Lithuania", "27.8", "26.3", "-5.2"], ["Luxembourg", "14.2", "11.1", "-21.8"], ["Latvia", "26.5", "27", "2"], ["Malta", "17.4", "17.9", "2.8"], ["Netherlands", "11.7", "10.1", "-14.1"], ["Poland", "14.7", "12.1", "-17.8"], ["Portugal", "14.2", "13.6", "-4.1"], ["Romania", "14.9", "11.9", "-20.4"], ["Sweden", "11", "9.2", "-16.3"], ["Slovenia", "9.3", "9.6", "3.9"], ["Slovakia", "17.4", "17", "-1.9"], ["United Kingdom", "10.8", "9.1", "-15.5"], ["European Union", "11.5", "9.7", "-15.1"]], "labels": {"name": "Country", "values": ["Austria", "Belgium", "Bulgaria", "Cyprus", "Czech Republic", "Germany", "Denmark", "Estonia", "Greece", "Spain", "Finland", "France", "Croatia", "Hungary", "Ireland", "Italy", "Lithuania", "Luxembourg", "Latvia", "Malta", "Netherlands", "Poland", "Portugal", "Romania", "Sweden", "Slovenia", "Slovakia", "United Kingdom", "European Union"]}, "metadata": {"link": "https://op.europa.eu/s/n2Du", "type": "Problem", "year": "2018", "title": "Total Piracy Trends by European Union Member State ", "topic": "Copyright Infringement", "method": "Administrative data", "source": "European Union Intellectual Property Office. Online Copyright Infringement in the European Union: Music, Films and TV (2017-2018), Trends and Drivers (Alicante: European Union Intellectual Property Office, 2019)", "sub_topic": "Prevalence of piracy", "chart_number": "144", "geographical": "European Union"}, "description": "The chart presents information on the total piracy trends by country, in the European Union. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [4.5, 4.8, 5, 5.5, 5.9, 6.8, 8, 9, 11], "name": "Billion, US dollars"}], "_data": [["Year", "Billion, US dollars"], ["2010", "4.5"], ["2011", "4.8"], ["2012", "5"], ["2013", "5.5"], ["2014", "5.9"], ["2015", "6.8"], ["2016", "8"], ["2017", "9"], ["2018", "11"]], "labels": {"name": "Year", "values": ["2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018"]}, "metadata": {"link": "https://www.ccianet.org/wp-content/uploads/2019/12/CCIA_Paper_Value_Growth_Music_Industry_2019_A4-2.pdf", "type": "Problem", "unit": "IFPI, Total Digital. Billion, US dollars", "year": "2010-2018", "title": "Growing Digital Revenue for Record Labels", "topic": "Copyright Infringement", "method": "Survey", "source": "De Posson, Victoria. \"Value Gap or Growth? How Digital Music Boosts Music Industry Growth,\" Computer and Communications Industry Association, December 2019", "sub_topic": "Growth of digital music revenues", "chart_number": "145", "geographical": "Global"}, "description": "In April 2019, International Federation of the Phonographic Industry (IFPI), which represents the recording industry worldwide, published its Global Music Report 2019 which includes its revenue figures for 2018. IFPI data shows a global revenue growth of 9.7% with a 32.9% increase in paid streaming revenues and 34% of growth in overall streaming revenue. These figures look quite different from the \"value gap\" the music industry is allegedly experiencing. The efficiency gains of the digitisation of music result in increased consumer welfare and revenues for record labels — a textbook illustration of a healthy, competitive market. The values in this chart are presented with approximation, as the original report does not provide the figures."},
{"data": [{"data": [4.71, 4.8, 4.86, 4.95, 5.3, 5.9, 6.6], "name": "Value"}], "_data": [["Year", "Value"], ["2012", "4.71"], ["2013", "4.80"], ["2014", "4.86"], ["2015", "4.95"], ["2016", "5.30"], ["2017", "5.90"], ["2018", "6.60"]], "labels": {"name": "Year", "values": ["2012", "2013", "2014", "2015", "2016", "2017", "2018"]}, "metadata": {"link": "https://www.ccianet.org/wp-content/uploads/2019/12/CCIA_Paper_Value_Growth_Music_Industry_2019_A4-2.pdf", "type": "Problem", "unit": "Value, Billion USD", "year": "2012-2018", "title": "Recorded Music, Wholesale Value (2012-2018)", "topic": "Copyright Infringement", "method": "Data collection", "source": "De Posson, Victoria. \"Value Gap or Growth? How Digital Music Boosts Music Industry Growth,\" Computer and Communications Industry Association, December 2019", "sub_topic": "Growth of digital music revenues", "chart_number": "146", "geographical": "Global"}, "description": "The chart presents the evolution of the music industry revenue, based on the report of the Recording Industry Association of America (RIAA), an industry group of major record labels. The results show that revenues measured at wholesale value grew\n12% compared to 2017, reaching to $6.6 billion in 2018. The report considers that the main drivers for the growth of record labels’ revenue are streaming music platforms."},
{"data": [{"data": [59.82, 46.83, 25.91, 36.17, 21.6, 51.05, 33.97, 29.13], "name": "The year on year growth"}], "_data": [["Year", "The year on year growth"], ["2011", "59.82"], ["2012", "46.83"], ["2013", "25.91"], ["2014", "36.17"], ["2015", "21.6"], ["2016", "51.05"], ["2017", "33.97"], ["2018", "29.13"]], "labels": {"name": "Year", "values": ["2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018"]}, "metadata": {"link": "https://www.ccianet.org/wp-content/uploads/2019/12/CCIA_Paper_Value_Growth_Music_Industry_2019_A4-2.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2012-2018", "title": "Digital Revenue", "topic": "Copyright Infringement", "method": "Survey", "source": "De Posson, Victoria. \"Value Gap or Growth? How Digital Music Boosts Music Industry Growth,\" Computer and Communications Industry Association, December 2019", "sub_topic": "Growth of digital music revenues", "chart_number": "147", "geographical": "Global"}, "description": "The chart shows that contrary to some popular beliefs, the Internet does not appear to have undone collecting societies. In 2018, digital income is the leading force behind the growth of music collections, accounting for 19.1% of the total. Digital income rose by 15% over the last year and 185% since 2014."},
{"data": [{"data": [46, 44, 39, 38, 35, 27, 24, 2, 2, 9], "name": "European Union, including United Kingdom"}, {"data": [46, 43, 38, 37, 36, 25, 23, 3, 3, 9], "name": "European Union, except United Kingdom"}], "_data": [["Question", "European Union, including United Kingdom", "European Union, except United Kingdom"], ["Help citizens to better identify disinformation", "46", "46"], ["Prevent those who spread disinformation from abusing social media platform services", "44", "43"], ["Regulate social media platforms to reduce the\ndistribution of disinformation", "39", "38"], ["Support fact-checking services ", "38", "37"], ["Support a diversity of information and quality journalism", "35", "36"], ["Make social media platforms explain to users why\nthey see personalised content", "27", "25"], ["Promote self-regulation (e.g. codes of conduct)\nby all actors, including social media platforms", "24", "23"], ["Nothing should be done", "2", "3"], ["Other (SPONTANEOUS)", "2", "3"], ["Don't know", "9", "9"]], "labels": {"name": "Question", "values": ["Help citizens to better identify disinformation", "Prevent those who spread disinformation from abusing social media platform services", "Regulate social media platforms to reduce the\ndistribution of disinformation", "Support fact-checking services ", "Support a diversity of information and quality journalism", "Make social media platforms explain to users why\nthey see personalised content", "Promote self-regulation (e.g. codes of conduct)\nby all actors, including social media platforms", "Nothing should be done", "Other (SPONTANEOUS)", "Don't know"]}, "metadata": {"link": "https://ec.europa.eu/commfrontoffice/publicopinion/index.cfm/survey/getsurveydetail/instruments/special/surveyky/2228", "type": "Solution", "unit": "Share of respondents (%)", "year": "2019", "title": "Opinions About Possible Measures Taken by the Public Authorities to Address Fake News or Disinformation", "topic": "Disinformation", "method": "Survey (N=27498)", "source": "European Commission. Special Eurobarometer 503: Attitudes Towards the Impact of Digitalisation on Daily Lives (Brussels: European Commission, 2020)", "sub_topic": "Removal of disinformation", "chart_number": "148", "geographical": "European Union"}, "description": "The chart shows the distribution of the responses to the question “In your opinion, which of the following measures should be taken by public authorities to address fake news or disinformation?\" of the participants to in the Special Eurobarometer survey conducted in December 2019. The question allows responded to select more than one answer. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [62, 53, 48, 28, 22, 2, 7], "name": "European Union, including United Kingdom"}, {"data": [61, 53, 46, 28, 21, 2, 7], "name": "European Union, except United Kingdom"}], "_data": [["Value", "European Union, including United Kingdom", "European Union, except United Kingdom"], ["The media", "62", "61"], ["Public authorities", "53", "53"], ["Social media platforms", "48", "46"], ["Citizens", "28", "28"], ["Educational institutions", "22", "21"], ["Other actors", "2", "2"], ["Don't know", "7", "7"]], "labels": {"name": "Value", "values": ["The media", "Public authorities", "Social media platforms", "Citizens", "Educational institutions", "Other actors", "Don't know"]}, "metadata": {"link": "https://ec.europa.eu/commfrontoffice/publicopinion/index.cfm/survey/getsurveydetail/instruments/special/surveyky/2228", "type": "Solution", "unit": "Share of respondents (%)", "year": "2019", "title": "Opinions About Organisations Responsible for Combating Fake News or Disinformation", "topic": "Disinformation", "method": "Survey (N=27498)", "source": "European Commission. Special Eurobarometer 503: Attitudes Towards the Impact of Digitalisation on Daily Lives (Brussels: European Commission, 2020)", "sub_topic": "Removal of disinformation", "chart_number": "149", "geographical": "European Union"}, "description": "This chart shows the distributions of finding of of a special Eurobarometer survey, conducted in December 2019. The respondents were asked which of the above-mentioned should be responsible for combatting fake news or disinformation and were allowed to select more than one option. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [30, 25, 17, 19, 9], "name": "Per cent (%) of respondents"}], "_data": [["Frequency", "Per cent (%) of respondents"], ["Every day or almost every day", "30"], ["At least once a week", "25"], ["Several times a month", "17"], ["Seldom or never", "19"], ["Don't know", "9"]], "labels": {"name": "Frequency", "values": ["Every day or almost every day", "At least once a week", "Several times a month", "Seldom or never", "Don't know"]}, "metadata": {"link": "https://ec.europa.eu/commfrontoffice/publicopinion/index.cfm/survey/getsurveydetail/instruments/special/surveyky/2228", "type": "Problem", "unit": "Per cent (%)", "year": "2019", "title": "Perception of the Frequency of Encountering News or Information Believed to Misrepresent Reality or be False at European Union Level", "topic": "Disinformation", "method": "Survey (N=27489)", "source": "European Commission. Special Eurobarometer 503: Attitudes Towards the Impact of Digitalisation on Daily Lives (Brussels: European Commission, 2020)", "sub_topic": "Prevalence of disinformation", "chart_number": "150", "geographical": "European Union"}, "description": "The findings of the Special Eurobarometer survey show that more than half of the respondents (55%) have came across news or information that they believe misrepresents reality or is false at least once a week. Moreover, one in three respondents encountered this type of information every day or almost every day. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [45, 32, 29, 35, 45, 45, 49, 39, 30, 33, 22, 27, 20, 29, 30, 14, 23, 31, 23, 19, 23, 22, 25, 31, 18, 17, 11, 18, 15], "name": "Every day or almost every day"}, {"data": [28, 30, 20, 29, 14, 21, 23, 27, 33, 29, 30, 29, 27, 28, 25, 35, 30, 26, 28, 29, 26, 26, 25, 12, 24, 24, 26, 22, 23], "name": "At least once a week"}, {"data": [12, 22, 35, 18, 23, 15, 8, 14, 17, 16, 25, 19, 27, 16, 17, 23, 19, 14, 19, 22, 19, 19, 17, 21, 22, 22, 22, 15, 15], "name": "Several times a month"}, {"data": [6, 11, 11, 16, 11, 11, 12, 15, 17, 16, 22, 20, 21, 20, 19, 12, 20, 22, 23, 17, 30, 25, 21, 22, 26, 24, 33, 27, 19], "name": "Seldom or never"}, {"data": [9, 5, 5, 2, 7, 8, 8, 5, 3, 6, 1, 5, 5, 7, 9, 16, 8, 7, 7, 13, 2, 8, 12, 14, 10, 13, 8, 18, 28], "name": "Don't know"}], "_data": [["Country", "Every day or almost every day", "At least once a week", "Several times a month", "Seldom or never", "Don't know"], ["Malta", "45", "28", "12", "6", "9"], ["Croatia", "32", "30", "22", "11", "5"], ["Greece", "29", "20", "35", "11", "5"], ["Netherlands", "35", "29", "18", "16", "2"], ["Cyprus", "45", "14", "23", "11", "7"], ["Spain", "45", "21", "15", "11", "8"], ["United Kingdom", "49", "23", "8", "12", "8"], ["France", "39", "27", "14", "15", "5"], ["Sweden", "30", "33", "17", "17", "3"], ["Luxembourg", "33", "29", "16", "16", "6"], ["Belgium", "22", "30", "25", "22", "1"], ["Danemark", "27", "29", "19", "20", "5"], ["Hungary", "20", "27", "27", "21", "5"], ["Slovenia", "29", "28", "16", "20", "7"], ["European Union", "30", "25", "17", "19", "9"], ["Slovakia", "14", "35", "23", "12", "16"], ["Lithuania", "23", "30", "19", "20", "8"], ["Latvia", "31", "26", "14", "22", "7"], ["Germany", "23", "28", "19", "23", "7"], ["Romania", "19", "29", "22", "17", "13"], ["Finland", "23", "26", "19", "30", "2"], ["Ireland", "22", "26", "19", "25", "8"], ["Estonia", "25", "25", "17", "21", "12"], ["Portugal", "31", "12", "21", "22", "14"], ["Czech Republic", "18", "24", "22", "26", "10"], ["Italy", "17", "24", "22", "24", "13"], ["Austria", "11", "26", "22", "33", "8"], ["Poland", "18", "22", "15", "27", "18"], ["Bulgaria", "15", "23", "15", "19", "28"]], "labels": {"name": "Country", "values": ["Malta", "Croatia", "Greece", "Netherlands", "Cyprus", "Spain", "United Kingdom", "France", "Sweden", "Luxembourg", "Belgium", "Danemark", "Hungary", "Slovenia", "European Union", "Slovakia", "Lithuania", "Latvia", "Germany", "Romania", "Finland", "Ireland", "Estonia", "Portugal", "Czech Republic", "Italy", "Austria", "Poland", "Bulgaria"]}, "metadata": {"link": "https://ec.europa.eu/commfrontoffice/publicopinion/index.cfm/survey/getsurveydetail/instruments/special/surveyky/2228", "type": "Problem", "unit": "Share of respondents  (%) ", "year": "2019", "title": "Perception of the Frequency of Encountering News or Information Believed to Misrepresent Reality or be False Across European Union Countries", "topic": "Disinformation", "method": "Survey (N=27498)", "source": "European Commission. Special Eurobarometer 503: Attitudes Towards the Impact of Digitalisation on Daily Lives (Brussels: European Commission, 2020)", "sub_topic": "Prevalence of disinformation", "chart_number": "151", "geographical": "European Union"}, "description": "The finding of the Special Eurobarometer 503 shows that 85% of Maltese respondents encountered news or information that they believe misrepresents reality or is false at least several times a month compared to only 53% in Bulgaria. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [3, 8.7, 21.5, 24], "name": "Increase in startup success rate"}], "_data": [["Country", "Increase in startup success rate"], ["Chile", "3,5"], ["Germany", "8.7"], ["India", "21.5"], ["Thailand", "24"]], "labels": {"name": "Country", "values": ["Chile", "Germany", "India", "Thailand"]}, "metadata": {"link": "https://www.oxera.com/wp-content/uploads/2018/07/The-economic-impact-of-safe-harbours-on-Internet-intermediary-start-ups.pdf.pdf", "type": "Solution", "unit": "Success rate (%)", "year": "2015", "title": "Estimated Impact of the Internet Intermediary Liability Regime on Startups’ Success Rate in Selected Countries (2015)", "topic": "Illegal Content", "method": "Data modelling", "source": "Oxera. The Economic Impact of Safe Harbours on Internet Intermediary Startups: Prepared for Google (Brussels:Oxera, 2015) ", "sub_topic": "Removal of illegal content", "chart_number": "152", "geographical": "Global"}, "description": "The chart presents an estimated impact on the success rate for startups in four selected countries – Chile, Germany, India and Thailand. The analysis suggests that a regime with clearly defined requirements for compliance and low associated compliance costs could increase startups’ success rates for intermediaries in the selected countries between 4% (Chile) and 24% (Thailand)."},
{"data": [{"data": [1.4, 2.9, 4.9, 2.3], "name": "Increase in expected success rate "}], "_data": [["Country", "Increase in expected success rate "], ["Chile", "1.4"], ["Germany", "2.9"], ["India", "4.9"], ["Thailand", "2.3"]], "labels": {"name": "Country", "values": ["Chile", "Germany", "India", "Thailand"]}, "metadata": {"link": "https://www.oxera.com/wp-content/uploads/2018/07/The-economic-impact-of-safe-harbours-on-Internet-intermediary-start-ups.pdf.pdf", "type": "Solution", "unit": "Success rate (%)", "year": "2015", "title": "Estimated Impact of the Internet Intermediary Liability Regime on Startups’ Success Rate in Selected Countries (2015)", "topic": "Illegal Content", "method": "Data modelling", "source": "Oxera. The Economic Impact of Safe Harbours on Internet Intermediary Startups: Prepared for Google (Brussels:Oxera, 2015) ", "sub_topic": "Removal of illegal content", "chart_number": "153", "geographical": "Global"}, "description": "The chart presents an estimated impact on expected profit for successful startups in four selected countries – Chile, Germany, India and Thailand. The analysis suggests that a regime with clearly defined requirements for compliance and low associated compliance costs could increase the startups’ expected profit for intermediaries in the focus countries between 1% (Chile) and 5% (India)."},
{"data": [{"data": [18, 19.6], "name": "Startup success rates"}], "_data": [["Intermediary liability regime", "Startup success rates"], ["Current intermediary liability regime", "18"], ["Increased liability protection", "19.6"]], "labels": {"name": "Intermediary liability regime", "values": ["Current intermediary liability regime", "Increased liability protection"]}, "metadata": {"link": "https://www.oxera.com/wp-content/uploads/2018/07/The-economic-impact-of-safe-harbours-on-Internet-intermediary-start-ups.pdf.pdf", "type": "Solution", "unit": "Success rate (%)", "year": "2015", "title": "Estimated Impact of the Internet Intermediary Liability Regime on Startups’ Success Rate in Germany (2015)", "topic": "Illegal Content", "method": "Data modelling", "source": "Oxera. The Economic Impact of Safe Harbours on Internet Intermediary Startups: Prepared for Google (Brussels:Oxera, 2015) ", "sub_topic": "Removal of illegal content", "chart_number": "154", "geographical": "Germany"}, "description": "Germany’s startup ecosystem could moderately benefit from increased liability protection in particular to increase its startup success rate. The model used by Oxera estimates that it could increase by 1.6%, translating into an increase of around 9% on its current success rate."},
{"data": [{"data": [967, 1640, 1033, 277, 343, 76, 47], "name": "European Union"}, {"data": [1576, 3362, 2337, 988, 1801, 469, 410], "name": "United States"}], "_data": [["Funding amount", "European Union", "United States"], ["None", "967", "1576"], ["<1m", "1640", "3362"], ["1m-5m", "1033", "2337"], ["5m-10m", "277", "988"], ["10m-50m", "343", "1801"], ["10m-100m", "76", "469"], [">100m", "47", "410"]], "labels": {"name": "Funding amount", "values": ["None", "<1m", "1m-5m", "5m-10m", "10m-50m", "10m-100m", ">100m"]}, "metadata": {"link": "https://copia.is/wp-content/uploads/2019/06/DSTMB-Copia.pdf", "type": "Problem", "unit": "Number of companies", "year": "2019", "title": "Internet Platform Funding Comparison: European Union and United States", "topic": "Illegal Content", "method": "Administrative data", "source": "Masnick, Michael. \"Don't Shoot the Message Board. How Intermediary Liability Harms Online Investment and Innovation,\"  Copia Institute and NetChoice, June 2019", "sub_topic": "Removal of illegal content", "chart_number": "155", "geographical": "Global"}, "description": "This chart shows a funding gap in the European Union compared to the United States. The data refers to a 15-year time horizon, considering companies formed after 01 January 2000 up until the end of 2014. The results suggest that a US-based company, under the framework set forth by the Communications Decency Act, Section 230, is five times more likely to secure investment over $10 million and nearly 10 times more likely to receive investments over $100 million, as compared to internet companies in the European Union, under the more limited E-Commerce Directive. Therefore, the internet platform companies built under the Communications Decency Act, Section 230 regime are much more likely to receive the significant investment necessary to grow and succeed."},
{"data": [{"data": [12.4, 8, 6.9, 5.6, 5.6, 5.3, 4.2, 4, 3.8, 3.7, 3.6, 3.5, 3.4, 3.3, 2.8, 2.5, 2.4, 2.3, 1.5], "name": "Percent of Gross Domestic Product"}], "_data": [["Country", "Percent of Gross Domestic Product"], ["United Kingdom", "12.40"], ["South Korea", "8"], ["China", "6.9"], ["India", "5.6"], ["Japan", "5.6"], ["United States", "5.3"], ["Mexico", "4.2"], ["Germany", "4"], ["Saudi Arabia", "3.8"], ["Canada", "3.7"], ["Australia", "3.6"], ["Italy", "3.5"], ["France", "3.4"], ["Argentina", "3.3"], ["Russia", "2.8"], ["South Africa", "2.5"], ["Brazil", "2.4"], ["Turkey", "2.3"], ["Indonesia", "1.5"]], "labels": {"name": "Country", "values": ["United Kingdom", "South Korea", "China", "India", "Japan", "United States", "Mexico", "Germany", "Saudi Arabia", "Canada", "Australia", "Italy", "France", "Argentina", "Russia", "South Africa", "Brazil", "Turkey", "Indonesia"]}, "metadata": {"link": "https://static1.squarespace.com/static/5481bc79e4b01c4bf3ceed80/t/5487f0d2e4b08e455df8388d/1418195154376/Fifth+Era+report+lr.pdf", "type": "Problem", "unit": "Percent (%)", "year": "2016", "title": "Internet Economy as Percentage of Gross Domestic Product (2016)", "topic": "Illegal Content", "method": "Administrative data", "source": "Le Merle, Matthew C., Tallulah J. Le Merle and Evan Engstrom. \"The Impact of Internet Regulation on Early Stage Investment, \"  Fifth Era, November, 2014", "sub_topic": "Removal of illegal content", "chart_number": "156", "geographical": "Global"}, "description": "This chart provides information on the share of the Internet economy within the gross domestic product for some selected countries. The data shows that Internet has created a tremendous amount of value for the economy globally, substantially impacting GDP in the selected countries."},
{"data": [{"data": [97, 93, 93, 93, 90, 83, 80, 78], "name": "Percent saying modest-strong negative impact"}], "_data": [["Country", "Percent saying modest-strong negative impact"], ["Spain", "97"], ["United States", "93"], ["United Kingdom", "93"], ["Australia", "93"], ["France", "90"], ["Italy", "83"], ["Germany", "80"], ["India", "78"]], "labels": {"name": "Country", "values": ["Spain", "United States", "United Kingdom", "Australia", "France", "Italy", "Germany", "India"]}, "metadata": {"link": "https://static1.squarespace.com/static/5481bc79e4b01c4bf3ceed80/t/5487f0d2e4b08e455df8388d/1418195154376/Fifth+Era+report+lr.pdf", "type": "Problem", "unit": "Percent of respondents (%)", "year": "2014", "Range": "0 100", "title": "The Legal Environment's Negative Impact on Investing (2014)", "topic": "Illegal Content", "method": "Survey (N =330)", "source": "Le Merle, Matthew C., Tallulah J. Le Merle and Evan Engstrom. \"The Impact of Internet Regulation on Early Stage Investment, \"  Fifth Era, November, 2014", "sub_topic": "Removal of illegal content", "chart_number": "157", "geographical": "Global"}, "description": "In the survey, investors were asked which of four factors had the most negative impact on their investing behavior: the legal environment, the economy, the competitive environment, or the expected return on their investment. The results show that in all eight countries, investors view the legal environment as having the most negative impact, with an average of 89% of investors surveyed saying it had a modest or strongly negative impact, 93% of United States investors feeling this way, and an average of 89% of the European Union investors concurring. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [97, 93, 90, 89, 87, 83, 80, 80], "name": "Share of investors for whom an ambiguous regulatory framework has a negative impact on their willingness to invest in digital content intermediaries that offer user-uploaded music or video."}], "_data": [["Country", "Share of investors for whom an ambiguous regulatory framework has a negative impact on their willingness to invest in digital content intermediaries that offer user-uploaded music or video."], ["Spain", "97.00"], ["United States", "93"], ["United Kingdom", "90"], ["Australia", "89"], ["France", "87"], ["Italy", "83"], ["Germany", "80"], ["India", "80"]], "labels": {"name": "Country", "values": ["Spain", "United States", "United Kingdom", "Australia", "France", "Italy", "Germany", "India"]}, "metadata": {"link": "https://static1.squarespace.com/static/5481bc79e4b01c4bf3ceed80/t/5487f0d2e4b08e455df8388d/1418195154376/Fifth+Era+report+lr.pdf", "type": "Problem", "unit": "Percent of investors (%)", "year": "2016", "title": "The Negative Impact on Investors of Regulatory Ambiguity (2014)", "topic": "Illegal Content", "method": "Survey (N =330)", "source": "Le Merle, Matthew C., Tallulah J. Le Merle and Evan Engstrom. \"The Impact of Internet Regulation on Early Stage Investment, \"  Fifth Era, November, 2014", "sub_topic": "Removal of illegal content", "chart_number": "158", "geographical": "Global"}, "description": "The chart shows that a high majority of respondents in every surveyed country consider that an ambiguous regulatory framework makes them uncomfortable investing in digital content intermediaries that offer user-uploaded music or video."},
{"data": [{"data": [90, 87, 87, 77, 76, 73, 73, 63], "name": "Percent who agree "}], "_data": [["Country", "Percent who agree "], ["United Kingdom", "90.00"], ["Spain", "87"], ["Italy", "87"], ["Australia", "77"], ["India", "76"], ["United States", "73"], ["France", "73"], ["Germany", "63"]], "labels": {"name": "Country", "values": ["United Kingdom", "Spain", "Italy", "Australia", "India", "United States", "France", "Germany"]}, "metadata": {"link": "https://static1.squarespace.com/static/5481bc79e4b01c4bf3ceed80/t/5487f0d2e4b08e455df8388d/1418195154376/Fifth+Era+report+lr.pdf", "type": "Problem", "unit": "Percent (%)", "year": "2016", "title": "Investors' Atitudes Towards Increased Antipiracy Regulations Against \"User Uploaded \" Websites ", "topic": "Copyright Infringement", "method": "Survey (N=330)", "source": "Le Merle, Matthew C., Tallulah J. Le Merle and Evan Engstrom. \"The Impact of Internet Regulation on Early Stage Investment,\"  Fifth Era, November, 2014", "sub_topic": "Prevalence of copyright infringement", "chart_number": "159", "geographical": "Global"}, "description": "The study found that 78% of investors would be deterred from investing in digital content intermediaries that offer user uploaded music or video should new anti-piracy regulations increase the risk that their investments would be exposed to secondary liability in intellectual property infringement cases. The respondents were asked if they agree with the statement \"Anti-piracy regulations against 'user uploaded' websites would deter my investment in digital content intermediaries that offer user-uploaded music or video.\""},
{"data": [{"data": [7, 3, 3, 3, 10, 3, 10, 13, 7, 10], "name": "Strongly disagree"}, {"data": [13, 10, 13, 40, 17, 40, 30, 23, 23, 27], "name": "Disagree"}, {"data": [23, 43, 37, 27, 46.5, 30, 33, 27, 40, 30], "name": "Agree"}, {"data": [57, 43, 43, 27, 26.5, 20, 27, 27, 30, 30], "name": "Strongly agree"}, {"data": [null, 1, 4, 3, null, 7, null, 10, null, 3], "name": "No response"}], "_data": [["Statement", "Strongly disagree", "Disagree", "Agree", "Strongly agree", "No response"], ["I am uncomfortable investing in business models in which the regulatory framework is ambiguous", "7", "13", "23", "57"], ["An ambiguous regulatory framework makes me uncomfortable investing in digital content intermediaries that offer user-uploaded music or video", "3", "10", "43", "43", "1"], ["Uncertain damages that are often large (in the event of liability) make me uncomfortable investing in digital content intermediaries in general", "3", "13", "37", "43", "4"], ["Increased anti-piracy regulations against 'user uploaded' websites would deter my investment in digital content intermediaries in general", "3", "40", "27", "27", "3"], ["Increased anti-piracy regulations against 'user uploaded' websites would deter my investment in digital content intermediaries that offer user-uploaded music or video", "10", "17", "46.5", "26.5"], ["I am uncomfortable investing in businesses which must receive regulatory approval for new product features that use previously collected user data", "3", "40", "30", "20", "7"], ["I am uncomfortable investing in businesses that would be required by law to run a technological filter on user-uploaded content", "10", "30", "33", "27"], ["I am uncomfortable investing in businesses that would be obligated to remove content upon receiving a request which must be evaluated by a person", "13", "23", "27", "27", "10"], ["I am uncomfortable investing in businesses that would be obligated to remove content upon receiving a request from an individual", "7", "23", "40", "30"], ["I am uncomfortable investing in businesses who would be legally obligated to store user data on servers located in the same country where users are located", "10", "27", "30", "30", "3"]], "labels": {"name": "Statement", "values": ["I am uncomfortable investing in business models in which the regulatory framework is ambiguous", "An ambiguous regulatory framework makes me uncomfortable investing in digital content intermediaries that offer user-uploaded music or video", "Uncertain damages that are often large (in the event of liability) make me uncomfortable investing in digital content intermediaries in general", "Increased anti-piracy regulations against 'user uploaded' websites would deter my investment in digital content intermediaries in general", "Increased anti-piracy regulations against 'user uploaded' websites would deter my investment in digital content intermediaries that offer user-uploaded music or video", "I am uncomfortable investing in businesses which must receive regulatory approval for new product features that use previously collected user data", "I am uncomfortable investing in businesses that would be required by law to run a technological filter on user-uploaded content", "I am uncomfortable investing in businesses that would be obligated to remove content upon receiving a request which must be evaluated by a person", "I am uncomfortable investing in businesses that would be obligated to remove content upon receiving a request from an individual", "I am uncomfortable investing in businesses who would be legally obligated to store user data on servers located in the same country where users are located"]}, "metadata": {"link": "https://static1.squarespace.com/static/5481bc79e4b01c4bf3ceed80/t/5487f0d2e4b08e455df8388d/1418195154376/Fifth+Era+report+lr.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2014", "title": "Investor Concern Regarding Potential New Regulation in France (2014)", "topic": "Illegal Content", "method": "Survey (N = 30)", "source": "Le Merle, Matthew C., Tallulah J. Le Merle and Evan Engstrom. \"The Impact of Internet Regulation on Early Stage Investment, \"  Fifth Era, November, 2014", "sub_topic": "Removal of illegal content", "chart_number": "160", "geographical": "France"}, "description": "According to the chart, 90% of French investors believe the legal environment has the most negative impact on their investing activities with a significant majority of 87% concerned about investing in digital content intermediaries that are today confronted by ambiguity and uncertain outcomes, potentially large damages, and the risks of secondary liability if new anti-piracy regulations are introduced."},
{"data": [{"data": [7, 3, 7, 10, 13, 7, 13, 13, 10, 7], "name": "Strongly disagree"}, {"data": [3, 7, 3, 17, 10, 23, 20, 13, 33, 27], "name": "Disagree"}, {"data": [23, 33, 50, 30, 33, 37, 30, 30, 33, 33], "name": "Agree"}, {"data": [60, 57, 33, 37, 30, 30, 37, 43, 17, 33], "name": "Strongly agree"}, {"data": [7, null, 7, 6, 14, 3, null, 1, 7, null], "name": "No response"}], "_data": [["Statement", "Strongly disagree", "Disagree", "Agree", "Strongly agree", "No response"], ["I am uncomfortable investing in business models in which the regulatory framework is ambiguous", "7", "3", "23", "60", "7"], ["An ambiguous regulatory framework makes me uncomfortable investing in digital content intermediaries that offer user-uploaded music or video", "3", "7", "33", "57"], ["Uncertain damages that are often large (in the event of liability) make me uncomfortable investing in digital content intermediaries in general", "7", "3", "50", "33", "7"], ["Increased anti-piracy regulations against 'user uploaded' websites would deter my investment in digital content intermediaries in general", "10", "17", "30", "37", "6"], ["Increased anti-piracy regulations against 'user uploaded' websites would deter my investment in digital content intermediaries that offer user-uploaded music or video", "13", "10", "33", "30", "14"], ["I am uncomfortable investing in businesses which must receive regulatory approval for new product features that use previously collected user data", "7", "23", "37", "30", "3"], ["I am uncomfortable investing in businesses that would be required by law to run a technological filter on user-uploaded content", "13", "20", "30", "37"], ["I am uncomfortable investing in businesses that would be obligated to remove content upon receiving a request which must be evaluated by a person", "13", "13", "30", "43", "1"], ["I am uncomfortable investing in businesses that would be obligated to remove content upon receiving a request from an individual", "10", "33", "33", "17", "7"], ["I am uncomfortable investing in businesses who would be legally obligated to store user data on servers located in the same country where users are located", "7", "27", "33", "33"]], "labels": {"name": "Statement", "values": ["I am uncomfortable investing in business models in which the regulatory framework is ambiguous", "An ambiguous regulatory framework makes me uncomfortable investing in digital content intermediaries that offer user-uploaded music or video", "Uncertain damages that are often large (in the event of liability) make me uncomfortable investing in digital content intermediaries in general", "Increased anti-piracy regulations against 'user uploaded' websites would deter my investment in digital content intermediaries in general", "Increased anti-piracy regulations against 'user uploaded' websites would deter my investment in digital content intermediaries that offer user-uploaded music or video", "I am uncomfortable investing in businesses which must receive regulatory approval for new product features that use previously collected user data", "I am uncomfortable investing in businesses that would be required by law to run a technological filter on user-uploaded content", "I am uncomfortable investing in businesses that would be obligated to remove content upon receiving a request which must be evaluated by a person", "I am uncomfortable investing in businesses that would be obligated to remove content upon receiving a request from an individual", "I am uncomfortable investing in businesses who would be legally obligated to store user data on servers located in the same country where users are located"]}, "metadata": {"link": "https://static1.squarespace.com/static/5481bc79e4b01c4bf3ceed80/t/5487f0d2e4b08e455df8388d/1418195154376/Fifth+Era+report+lr.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2014", "title": "Investor Concern Regarding Potential New Regulation in Germany (2014)", "topic": "Illegal Content", "method": "Survey (N = 30)", "source": "Le Merle, Matthew C., Tallulah J. Le Merle and Evan Engstrom. \"The Impact of Internet Regulation on Early Stage Investment, \"  Fifth Era, November, 2014", "sub_topic": "Removal of illegal content", "chart_number": "161", "geographical": "Germany"}, "description": "According to the chart, 80% of German investors believe the legal environment has the most negative impact on their investing activities with a significant majority of 90% concerned about investing in digital content intermediaries that are today confronted by ambiguity and uncertain outcomes, potentially large damages, and the risks of secondary liability if new anti-piracy regulations are introduced."},
{"data": [{"data": [10, 10, 7, 7, 10, 10, 7, 7, 7, null], "name": "Strongly disagree"}, {"data": [7, 7, 10, 27, 3, 20, 23, 20, 20, 27], "name": "Disagree"}, {"data": [30, 43, 37, 30, 50, 27, 33, 30, 40, 27], "name": "Agree"}, {"data": [50, 40, 43, 27, 37, 40, 33, 43, 30, 37], "name": "Strongly agree"}, {"data": [3, null, 3, 9, null, 3, 4, null, 3, 9], "name": "No response"}], "_data": [["Statement", "Strongly disagree", "Disagree", "Agree", "Strongly agree", "No response"], ["I am uncomfortable investing in business models in which the regulatory framework is ambiguous", "10", "7", "30", "50", "3"], ["An ambiguous regulatory framework makes me uncomfortable investing in digital content intermediaries that offer user-uploaded music or video", "10", "7", "43", "40"], ["Uncertain damages that are often large (in the event of liability) make me uncomfortable investing in digital content intermediaries in general", "7", "10", "37", "43", "3"], ["Increased anti-piracy regulations against 'user uploaded' websites would deter my investment in digital content intermediaries in general", "7", "27", "30", "27", "9"], ["Increased anti-piracy regulations against 'user uploaded' websites would deter my investment in digital content intermediaries that offer user-uploaded music or video", "10", "3", "50", "37"], ["I am uncomfortable investing in businesses which must receive regulatory approval for new product features that use previously collected user data", "10", "20", "27", "40", "3"], ["I am uncomfortable investing in businesses that would be required by law to run a technological filter on user-uploaded content", "7", "23", "33", "33", "4"], ["I am uncomfortable investing in businesses that would be obligated to remove content upon receiving a request which must be evaluated by a person", "7", "20", "30", "43"], ["I am uncomfortable investing in businesses that would be obligated to remove content upon receiving a request from an individual", "7", "20", "40", "30", "3"], ["I am uncomfortable investing in businesses who would be legally obligated to store user data on servers located in the same country where users are located", "0", "27", "27", "37", "9"]], "labels": {"name": "Statement", "values": ["I am uncomfortable investing in business models in which the regulatory framework is ambiguous", "An ambiguous regulatory framework makes me uncomfortable investing in digital content intermediaries that offer user-uploaded music or video", "Uncertain damages that are often large (in the event of liability) make me uncomfortable investing in digital content intermediaries in general", "Increased anti-piracy regulations against 'user uploaded' websites would deter my investment in digital content intermediaries in general", "Increased anti-piracy regulations against 'user uploaded' websites would deter my investment in digital content intermediaries that offer user-uploaded music or video", "I am uncomfortable investing in businesses which must receive regulatory approval for new product features that use previously collected user data", "I am uncomfortable investing in businesses that would be required by law to run a technological filter on user-uploaded content", "I am uncomfortable investing in businesses that would be obligated to remove content upon receiving a request which must be evaluated by a person", "I am uncomfortable investing in businesses that would be obligated to remove content upon receiving a request from an individual", "I am uncomfortable investing in businesses who would be legally obligated to store user data on servers located in the same country where users are located"]}, "metadata": {"link": "https://static1.squarespace.com/static/5481bc79e4b01c4bf3ceed80/t/5487f0d2e4b08e455df8388d/1418195154376/Fifth+Era+report+lr.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2016", "title": "Investor Concern Regarding Potential New Regulation in Italy (2014)", "topic": "Illegal Content", "method": "Survey (N = 30)", "source": "Le Merle, Matthew C., Tallulah J. Le Merle and Evan Engstrom. \"The Impact of Internet Regulation on Early Stage Investment, \"  Fifth Era, November, 2014", "sub_topic": "Removal of illegal content", "chart_number": "162", "geographical": "Italy"}, "description": "In summary, 83% of Italian investors believe the legal environment has the most negative impact on their investing activities with a significant majority of 83% concerned about investing in digital content intermediaries that are today confronted by ambiguity and uncertain outcomes, potentially large damages, and the risks of secondary liability if new anti-piracy regulations are introduced."},
{"data": [{"data": [3, 3, 3, 10, 7, 3, 7, 10, null, null], "name": "Strongly disagree"}, {"data": [7, 3, 3, 3, 3, 13, 27, 20, 20, 27], "name": "Disagree"}, {"data": [33, 40, 40, 50, 37, 43, 40, 53, 57, 33], "name": "Agree"}, {"data": [57, 53, 50, 33, 50, 37, 27, 17, 23, 37], "name": "Strongly agree"}, {"data": [null, 1, 4, 4, 3, 4, null, null, null, 3], "name": "No response"}], "_data": [["Statement", "Strongly disagree", "Disagree", "Agree", "Strongly agree", "No response"], ["I am uncomfortable investing in business models in which the regulatory framework is ambiguous", "3.00", "7.00", "33.00", "57.00"], ["An ambiguous regulatory framework makes me uncomfortable investing in digital content intermediaries that offer user-uploaded music or video", "3", "3", "40", "53", "1"], ["Uncertain damages that are often large (in the event of liability) make me uncomfortable investing in digital content intermediaries in general", "3", "3", "40", "50", "4"], ["Increased anti-piracy regulations against \"user uploaded\" websites would deter my investment in digital content intermediaries in general", "10", "3", "50", "33", "4"], ["Increased anti-piracy regulations against \"user uploaded\" websites would deter my investment in digital content intermediaries that offer user-uploaded music or video", "7", "3", "37", "50", "3"], ["I am uncomfortable investing in businesses which must receive regulatory approval for new product features that use previously collected user data", "3", "13", "43", "37", "4"], ["I am uncomfortable investing in businesses that would be required by law to run a technological filter on user-uploaded content", "7", "27", "40", "27"], ["I am uncomfortable investing in businesses that would be obligated to remove content upon receiving a request which must be evaluated by a person", "10", "20", "53", "17"], ["I am uncomfortable investing in businesses that would be obligated to remove content upon receiving a request from an individual", "0", "20", "57", "23"], ["I am uncomfortable investing in businesses who would be legally obligated to store user data on servers located in the same country where users are located", "0", "27", "33", "37", "3"]], "labels": {"name": "Statement", "values": ["I am uncomfortable investing in business models in which the regulatory framework is ambiguous", "An ambiguous regulatory framework makes me uncomfortable investing in digital content intermediaries that offer user-uploaded music or video", "Uncertain damages that are often large (in the event of liability) make me uncomfortable investing in digital content intermediaries in general", "Increased anti-piracy regulations against \"user uploaded\" websites would deter my investment in digital content intermediaries in general", "Increased anti-piracy regulations against \"user uploaded\" websites would deter my investment in digital content intermediaries that offer user-uploaded music or video", "I am uncomfortable investing in businesses which must receive regulatory approval for new product features that use previously collected user data", "I am uncomfortable investing in businesses that would be required by law to run a technological filter on user-uploaded content", "I am uncomfortable investing in businesses that would be obligated to remove content upon receiving a request which must be evaluated by a person", "I am uncomfortable investing in businesses that would be obligated to remove content upon receiving a request from an individual", "I am uncomfortable investing in businesses who would be legally obligated to store user data on servers located in the same country where users are located"]}, "metadata": {"link": "https://static1.squarespace.com/static/5481bc79e4b01c4bf3ceed80/t/5487f0d2e4b08e455df8388d/1418195154376/Fifth+Era+report+lr.pdf", "type": "Problem", "year": "2016", "title": "Investor Concern Regarding Potential New Regulation in Spain (2014)", "topic": "Illegal Content", "method": "Survey (N = 30)", "source": "Le Merle, Matthew C., Tallulah J. Le Merle and Evan Engstrom. \"The Impact of Internet Regulation on Early Stage Investment, \"  Fifth Era, November, 2014", "sub_topic": "Removal of illegal content", "chart_number": "163", "geographical": "Spain"}, "description": "In summary, 97% of Spanish investors believe the legal environment has the most negative impact on their investing activities with a significant majority of 93% concerned about investing in digital content intermediaries that are today confronted by ambiguity and uncertain outcomes, potentially large damages, and the risks of secondary liability if new anti-piracy regulations are introduced."},
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{"data": [{"data": [49.04, 31.73, 6.1, 13.13], "name": "Percent "}], "_data": [["Product", "Percent "], ["YouTube", "49.04%"], ["Web search", "31.73%"], ["Blogger", "6.10%"], ["All others", "13.13%"]], "labels": {"name": "Product", "values": ["YouTube", "Web search", "Blogger", "All others"]}, "metadata": {"link": "https://transparencyreport.google.com/government-removals/overview", "type": "Solution", "unit": "Percent (%)", "year": "2009-2021", "title": "Products Affected by Government Requests of Removal", "topic": "Illegal Content", "method": "Self-reporting", "source": "Google. Transparency Report: Government Requests to Remove Content (www.google.com, 2022)", "sub_topic": "Removal of illegal content", "chart_number": "166", "geographical": "Global"}, "description": "The chart presents the distribution of the most affected products by governments' requests of removal, since 2009. The shares are calculated based on the total numbers of requests received by Google from governments since July 2009 until December 2021. The data shows that the products with the most frequent government requests to remove content are YouTube, Web search and Blogger, but other products are also affected."},
{"data": [{"data": [22.9, 21.2, 20, 19, 17.8, 16.1, 14, 11.8, 10.3, 8.9, 8.1, 7.5, 6.7, 5.9, 5.7, 5.5, 5.2, 4.6, 4.4], "name": "Total physical"}, {"data": [0, 0, 0, 0, 0, 0, 0.1, 0.2, 0.3, 0.4, 0.6, 1.8, 1.4, 1.9, 2.8, 4.6, 6.5, 9.2, 11.4], "name": "Total streaming"}, {"data": [null, null, null, 0.4, 1, 2.8, 2.7, 3.4, 3.7, 3.9, 4.2, 4.4, 4.3, 4, 3.7, 3.2, 2.6, 1.7, 1.5], "name": "Downloads and other digital"}, {"data": [0.6, 0.7, 0.8, 0.9, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.4, 1.6, 1.8, 1.9, 1.9, 2.2, 2.3, 2.6, 2.6], "name": "Performance rights"}, {"data": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0.3, 0.3, 0.3, 0.3, 0.4, 0.4, 0.4, 0.5, 0.5], "name": "Synchronisation"}], "_data": [["Year", "Total physical", "Total streaming", "Downloads and other digital", "Performance rights", "Synchronisation"], ["2001", "22.9", "0.00", "0", "0.6", "0.00"], ["2002", "21.2", "0.00", "0", "0.7", "0.00"], ["2003", "20", "0.00", "0", "0.8", "0.00"], ["2004", "19", "0.00", "0.4", "0.9", "0.00"], ["2005", "17.8", "0.00", "1", "0.9", "0.00"], ["2006", "16.1", "0.00", "2.8", "1", "0.00"], ["2007", "14", "0.1", "2.7", "1.1", "0.00"], ["2008", "11.8", "0.2", "3.4", "1.2", "0.00"], ["2009", "10.3", "0.3", "3.7", "1.3", "0.00"], ["2010", "8.9", "0.4", "3.9", "1.4", "0.3"], ["2011", "8.1", "0.6", "4.2", "1.4", "0.3"], ["2012", "7.5", "1.8", "4.4", "1.6", "0.3"], ["2013", "6.7", "1.4", "4.3", "1.8", "0.3"], ["2014", "5.9", "1.9", "4", "1.9", "0.3"], ["2015", "5.7", "2.8", "3.7", "1.9", "0.4"], ["2016", "5.5", "4.6", "3.2", "2.2", "0.4"], ["2017", "5.2", "6.5", "2.6", "2.3", "0.4"], ["2018", "4.6", "9.2", "1.7", "2.6", "0.5"], ["2019", "4.4", "11.4", "1.5", "2.6", "0.5"]], "labels": {"name": "Year", "values": ["2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019"]}, "metadata": {"link": "https://www.ifpi.org/news/IFPI-issues-annual-Global-Music-Report", "type": "Problem", "unit": "Revenue (US Dollars, Billions)", "year": "2019", "title": "Global Recorded Music Industry Revenues (2001-2019)", "topic": "Copyright Infringement", "method": "Data collection", "source": "International Federation of the Phonographic Industry. Global Music Report 2020 (London: International Federation of the Phonographic Industry, 2020)", "sub_topic": "Growth of digital music revenues", "chart_number": "177", "geographical": "Global"}, "description": "According to the International Federation of the Phonographic Industry report, in 2019, the global recorded music market grew by 8.2%, its fifth consecutive year of growth. The growth was predominantly driven by fans’ increasing engagement with music on paid streaming services, with the number of paid streaming accounts rising to 341 million by the end of 2019 and associated revenue increasing by 24.1%."},
{"data": [{"data": [95, 94, 87, 80, 77, 77, 77, 76, 76, 75, 75, 73, 72, 72, 72, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], "name": "Free"}, {"data": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 68, 67, 66, 64, 64, 64, 64, 64, 61, 61, 60, 58, 57, 57, 56, 56, 56, 55, 54, 52, 51, 49, 49, 48, 47, 44, 43, 42, 41, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], "name": "Partly free"}, {"data": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 39, 37, 36, 35, 35, 32, 31, 30, 29, 28, 28, 26, 26, 26, 25, 25, 24, 22, 17, 15, 10], "name": "Not free"}], "_data": [["Country", "Free", "Partly free", "Not free"], ["Iceland ", "95", "0", "0"], ["Estonia", "94", "0", "0"], ["Canada", "87", "0", "0"], ["Germany", "80", "0", "0"], ["Australia", "77", "0", "0"], ["United Kingdom", "77", "0", "0"], ["United States", "77", "0", "0"], ["Armenia", "76", "0", "0"], ["France", "76", "0", "0"], ["Georgia", "75", "0", "0"], ["Italy", "75", "0", "0"], ["Japan", "73", "0", "0"], ["Argentina", "72", "0", "0"], ["Hungary", "72", "0", "0"], ["South Africa", "72", "0", "0"], ["Kenya", "0", "68", "0"], ["Colombia", "0", "67", "0"], ["Philippines", "0", "66", "0"], ["Angola", "0", "64", "0"], ["Brazil", "0", "64", "0"], ["Nigeria", "0", "64", "0"], ["South Korea", "0", "64", "0"], ["Tunisia", "0", "64", "0"], ["Ecuador", "0", "61", "0"], ["Kyrgystan", "0", "61", "0"], ["Mexico", "0", "60", "0"], ["Zambia", "0", "58", "0"], ["Malawi", "0", "57", "0"], ["Malaysia", "0", "57", "0"], ["Singapore", "0", "56", "0"], ["Uganda", "0", "56", "0"], ["Ukraine", "0", "56", "0"], ["India", "0", "55", "0"], ["Morocco", "0", "54", "0"], ["Lebanon", "0", "52", "0"], ["Indonesia", "0", "51", "0"], ["Libya", "0", "49", "0"], ["Sri Lanka", "0", "49", "0"], ["The Gambia", "0", "48", "0"], ["Jordan", "0", "47", "0"], ["Bangladesh", "0", "44", "0"], ["Cambodia", "0", "43", "0"], ["Zimbabwe", "0", "42", "0"], ["Rwanda", "0", "41", "0"], ["Azerbaijan", "0", "0", "39"], ["Turkey", "0", "0", "37"], ["Myanmar", "0", "0", "36"], ["Belarus", "0", "0", "35"], ["Thailand", "0", "0", "35"], ["Kazahstan", "0", "0", "32"], ["Russia", "0", "0", "31"], ["Venezuela", "0", "0", "30"], ["Bahrain", "0", "0", "29"], ["Ethiopia", "0", "0", "28"], ["United Arab Emirates", "0", "0", "28"], ["Egypt", "0", "0", "26"], ["Pakistan", "0", "0", "26"], ["Uzbekistan", "0", "0", "26"], ["Saudi Arabia", "0", "0", "25"], ["Sudan", "0", "0", "25"], ["Vietnam", "0", "0", "24"], ["Cuba", "0", "0", "22"], ["Syria", "0", "0", "17"], ["Iran", "0", "0", "15"], ["China", "0", "0", "10"]], "labels": {"name": "Country", "values": ["Iceland ", "Estonia", "Canada", "Germany", "Australia", "United Kingdom", "United States", "Armenia", "France", "Georgia", "Italy", "Japan", "Argentina", "Hungary", "South Africa", "Kenya", "Colombia", "Philippines", "Angola", "Brazil", "Nigeria", "South Korea", "Tunisia", "Ecuador", "Kyrgystan", "Mexico", "Zambia", "Malawi", "Malaysia", "Singapore", "Uganda", "Ukraine", "India", "Morocco", "Lebanon", "Indonesia", "Libya", "Sri Lanka", "The Gambia", "Jordan", "Bangladesh", "Cambodia", "Zimbabwe", "Rwanda", "Azerbaijan", "Turkey", "Myanmar", "Belarus", "Thailand", "Kazahstan", "Russia", "Venezuela", "Bahrain", "Ethiopia", "United Arab Emirates", "Egypt", "Pakistan", "Uzbekistan", "Saudi Arabia", "Sudan", "Vietnam", "Cuba", "Syria", "Iran", "China"]}, "metadata": {"link": "https://freedomhouse.org/sites/default/files/2019-11/11042019_Report_FH_FOTN_2019_final_Public_Download.pdf", "type": "Problem", "unit": "Ranking ( 0 = Least Free 100 = Most Free)", "year": "2019", "title": "Global Rankings of the Level of Internet and Digital Media Freedom", "topic": "Illegal Content", "method": "Data collection", "source": "Shahbaz, Adrian and Allie Funk. \"Freedom on the Net 2019: The Crisis of Social Media, \" Freedom House, 2019", "sub_topic": "Prevalence of illegal content", "chart_number": "171", "geographical": "Global"}, "description": "Freedom on the Net measures the level of internet and digital media freedom in 65 countries (for a full display of countries, please view the chart in full screen). Each country receives a numerical score from 100 (the most free) to 0 (the least free), which serves as the basis for an internet freedom status designation of <b>free </b>(70–100 points), <b> partly free </b>(40–69 points) or <b>not free</b> (0–39 points). Ratings are determined through an examination of three broad categories: <b>obstacles to access</b> (assesses infrastructural and economic barriers to access; government efforts to block specific applications or technologies; and legal, regulatory, and ownership control over internet and mobile phone access providers); <b>limits on content</b> (examines filtering and blocking of websites; other forms of censorship and self-censorship; manipulation of content; the diversity of online news media; and usage of digital media for social and political activism); <b>violations of user rights </b> (measures legal protections and restrictions on online activity; surveillance; privacy; and repercussions for online activity, such as legal prosecution, imprisonment, physical attacks, or other forms of harassment)."},
{"data": [{"data": [79, 70, 47, 34, 17], "name": "United Kingdom"}, {"data": [72, 58, 47, 20, 13], "name": "United States"}, {"data": [69, 72, 39, 41, 22], "name": "Germany"}, {"data": [83, 71, 63, 24, 26], "name": "Spain"}, {"data": [85, 65, 51, 14, 17], "name": "South Korea"}, {"data": [90, 77, 78, 24, 28], "name": "Argentina"}], "_data": [["Channel", "United Kingdom", "United States", "Germany", "Spain", "South Korea", "Argentina"], ["Online (including social)", "79", "72", "69", "83", "85", "90"], ["Television", "70", "58", "72", "71", "65", "77"], ["Social media", "47", "47", "39", "63", "51", "78"], ["Radio", "34", "20", "41", "24", "14", "24"], ["Newspapers", "17", "13", "22", "26", "17", "28"]], "labels": {"name": "Channel", "values": ["Online (including social)", "Television", "Social media", "Radio", "Newspapers"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2020-04/Navigating%20the%20Coronavirus%20Infodemic%20FINAL.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2020", "title": "Distribution of Mass Media as a Source of Getting News in the Last Week", "topic": "Disinformation", "method": "Survey ( United Kingdom (N=2216), United States (N= 1273), Germany (N=2003), Spain (N=1018), South Korea (N=1009), Argentina (N= 1003))", "source": "Nielsen, Rasmus Kleis, Richard Fletcher, Nic Newman, J. Scott Brennen, and Philip N. Howard. \"Navigating the ‘Infodemic’: How People in Six Countries Access and Rate News and Information About Coronavirus,\"  Reuters Institute, April 2020", "sub_topic": "Trust in sources of news", "chart_number": "172", "geographical": "Global"}, "description": "The chart presents the distribution of different mass media (television, radio, etc.) as source of news during the coronavirus lockdown. The participants in six countries have answered to the following question \"Q4: Which, if any, of the following have you used in the last week as a source of news?\" Television and online are the most popular way of getting news in all six countries. The figures for newspapers are lower than normal, as countries have entered lockdown, complicating print distribution and greatly reducing single copies sales. "},
{"data": [{"data": [59, 56, 48, 35, 29, 18, 9, 6], "name": "United Kingdom"}, {"data": [54, 35, 29, 49, 32, 25, 14, 11], "name": "United States"}, {"data": [47, 33, 21, 44, 24, 23, 16, 7], "name": "Germany"}, {"data": [74, 39, 31, 39, 33, 13, 18, 8], "name": "Spain"}, {"data": [77, 31, 37, 21, 16, 19, 6, 7], "name": "South Korea"}, {"data": [74, 52, 46, 45, 43, 18, 16, 7], "name": "Argentina"}], "_data": [["Channel", "United Kingdom", "United States", "Germany", "Spain", "South Korea", "Argentina"], ["News organisations", "59", "54", "47", "74", "77", "74"], ["National government", "56", "35", "33", "39", "31", "52"], ["National health organisations", "48", "29", "21", "31", "37", "46"], ["Scientists, doctors, health experts", "35", "49", "44", "39", "21", "45"], ["Global health organisations", "29", "32", "24", "33", "16", "43"], ["Ordinary people I know personally", "18", "25", "23", "13", "19", "18"], ["Politicians", "9", "14", "16", "18", "6", "16"], ["Ordinary people I do not know personally", "6", "11", "7", "8", "7", "7"]], "labels": {"name": "Channel", "values": ["News organisations", "National government", "National health organisations", "Scientists, doctors, health experts", "Global health organisations", "Ordinary people I know personally", "Politicians", "Ordinary people I do not know personally"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2020-04/Navigating%20the%20Coronavirus%20Infodemic%20FINAL.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2020", "title": "Distribution of Sources Used for Coronavirus News in the Last Week", "topic": "Disinformation", "method": "Survey ( United Kingdom (N=2216), United States (N= 1273), Germany (N=2003), Spain (N=1018), South Korea (N=1009), Argentina (N= 1003)) ", "source": "Nielsen, Rasmus Kleis, Richard Fletcher, Nic Newman, J. Scott Brennen, and Philip N. Howard. \"Navigating the ‘Infodemic’: How People in Six Countries Access and Rate News and Information About Coronavirus,\"  Reuters Institute, April 2020", "sub_topic": "Trust in sources of news", "chart_number": "173", "geographical": "Global"}, "description": "The chart presents the distribution of sources used to obtain information about coronavirus (COVID-19) by repondents in six countries surveyed. The participants have answered to the following question \"Q4: Which, if any, of the following have you used in the last week as a source of news or information about coronavirus (COVID-19)?\" According to the results, in April 2020, news organisations remain among the most important sources of information. In a public health crisis, where most people are online and many diferrent organisations, including public authorities, have websites, social media accounts, and other channels available, news media are not the only sources people rely on. Across the six countries surveyed, two-thirds have relied on news organisations, ranging from a low 47% in Germany to a high 77% in South Korea. "},
{"data": [{"data": [89, 87, 84, 69, 60, 41, 35, 10], "name": "Trust "}, {"data": [5, 7, 7, 11, 21, 26, 37, 34], "name": "Neither"}, {"data": [3, 2, 4, 2, 4, 5, 4, 6], "name": "Don't know"}, {"data": [3, 4, 5, 18, 15, 28, 24, 50], "name": "Does not trust"}], "_data": [["Channel", "Trust ", "Neither", "Don't know", "Does not trust"], ["National health organisations", "89", "5", "3", "3"], ["Scientists, doctors, health experts", "87", "7", "2", "4"], ["Global health organisations", "84", "7", "4", "5"], ["Government", "69", "11", "2", "18"], ["News organisations", "60", "21", "4", "15"], ["Politicians", "41", "26", "5", "28"], ["People I know", "35", "37", "4", "24"], ["People I don't know", "10", "34", "6", "50"]], "labels": {"name": "Channel", "values": ["National health organisations", "Scientists, doctors, health experts", "Global health organisations", "Government", "News organisations", "Politicians", "People I know", "People I don't know"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2020-04/Navigating%20the%20Coronavirus%20Infodemic%20FINAL.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2020", "title": "Distribution of Sources Used for Coronavirus News in the United Kingdom", "topic": "Disinformation", "method": "Survey (N=2216) ", "source": "Nielsen, Rasmus Kleis, Richard Fletcher, Nic Newman, J. Scott Brennen, and Philip N. Howard. \"Navigating the ‘Infodemic’: How People in Six Countries Access and Rate News and Information About Coronavirus,\"  Reuters Institute, April 2020", "sub_topic": "Trust in sources of news", "chart_number": "174", "geographical": "United Kingdom"}, "description": "The chart shows that United Kingdom respondents trust the most national health organisations when it comes to getting information about coronavirus (89%) and trust the least people they don't know (10%). The results are based on the participants' answers to the following question \"Q10: How trustworthy would you say news and information about coronavirus (COVID-19) from the following is? Please use the scale below where 0 is \"not at all trustworthy\" and 10 is \"completely trustworthy.\"\""},
{"data": [{"data": [74, 68, 64, 59, 58, 45, 39, 15], "name": "Trust "}, {"data": [12, 15, 16, 17, 20, 29, 27, 33], "name": "Neither"}, {"data": [6, 6, 7, 6, 6, 7, 7, 11], "name": "Don't know"}, {"data": [8, 11, 13, 18, 15, 18, 26, 41], "name": "Does not trust"}, {"data": [null, null, null, null, 1, 1, 1, null], "name": "No response"}], "_data": [["Channel", "Trust ", "Neither", "Don't know", "Does not trust", "No response"], ["Scientists, doctors, health experts", "74", "12", "6", "8", "0"], ["National health organisations", "68", "15", "6", "11", "0"], ["Global health organisations", "64", "16", "7", "13", "0"], ["Government", "59", "17", "6", "18", "0"], ["News organisations", "58", "20", "6", "15", "1"], ["People I know", "45", "29", "7", "18", "1"], ["Politicians", "39", "27", "7", "26", "1"], ["People I don't know", "15", "33", "11", "41", "0"]], "labels": {"name": "Channel", "values": ["Scientists, doctors, health experts", "National health organisations", "Global health organisations", "Government", "News organisations", "People I know", "Politicians", "People I don't know"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2020-04/Navigating%20the%20Coronavirus%20Infodemic%20FINAL.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2020", "title": "Distribution of Sources Used for Coronavirus News in Germany", "topic": "Disinformation", "method": "Survey (N=2003)", "source": "Nielsen, Rasmus Kleis, Richard Fletcher, Nic Newman, J. Scott Brennen, and Philip N. Howard. \"Navigating the ‘Infodemic’: How People in Six Countries Access and Rate News and Information About Coronavirus,\"  Reuters Institute, April 2020", "sub_topic": "Trust in sources of news", "chart_number": "175", "geographical": "Germany"}, "description": "The chart shows that German respondents trust the most scientists, doctors and health experts when it comes to getting information about coronavirus (74%) and trust the least people they don't know (15%). The results are based on the participants' answers to the following question \"Q10: How trustworthy would you say news and information about coronavirus (COVID-19) from the following is? Please use the scale below where 0 is \"not at all trustworthy\" and 10 is \"completely trustworthy.\"\""},
{"data": [{"data": [84, 77, 69, 51, 46, 33, 31, 16], "name": "Trust "}, {"data": [10, 12, 17, 24, 16, 33, 22, 29], "name": "Neither"}, {"data": [2, 2, 2, 2, 3, 3, 2, 3], "name": "Don't know"}, {"data": [4, 9, 12, 23, 35, 31, 45, 52], "name": "Does not trust"}], "_data": [["Channel", "Trust ", "Neither", "Don't know", "Does not trust"], ["Scientists, doctors, health experts", "84", "10", "2", "4"], ["Global health organisations", "77", "12", "2", "9"], ["National health organisations", "69", "17", "2", "12"], ["News organisations", "51", "24", "2", "23"], ["Government", "46", "16", "3", "35"], ["People I know", "33", "33", "3", "31"], ["Politicians", "31", "22", "2", "45"], ["People I don't know", "16", "29", "3", "52"]], "labels": {"name": "Channel", "values": ["Scientists, doctors, health experts", "Global health organisations", "National health organisations", "News organisations", "Government", "People I know", "Politicians", "People I don't know"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2020-04/Navigating%20the%20Coronavirus%20Infodemic%20FINAL.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2020", "title": "Distribution of Sources Used for Coronavirus News in Spain", "topic": "Disinformation", "method": "Survey (N=2003)", "source": "Nielsen, Rasmus Kleis, Richard Fletcher, Nic Newman, J. Scott Brennen, and Philip N. Howard. \"Navigating the ‘Infodemic’: How People in Six Countries Access and Rate News and Information About Coronavirus,\"  Reuters Institute, April 2020", "sub_topic": "Trust in sources of news", "chart_number": "176", "geographical": "Spain"}, "description": "The chart shows that Spanish respondents trust the most scientists, doctors and health experts when it comes to getting information about coronavirus (84%) and trust the least people they don't know (16%). The results are based on the participants' answers to the following question \"Q10: How trustworthy would you say news and information about coronavirus (COVID-19) from the following is? Please use the scale below where 0 is \"not at all trustworthy\" and 10 is \"completely trustworthy.\"\""},
{"data": [{"data": [56, 56, 55, 52, 51, 50, 48, 48, 46, 45, 45, 45, 45, 44, 44, 40, 39, 39, 38, 38, 38, 37, 36, 36, 33, 33, 33, 30, 30, 29, 29, 28, 28, 28, 27, 27, 25, 24, 23, 21], "name": "Percent Agreed"}], "_data": [["Country", "Percent Agreed"], ["Finland", "56"], ["Portugal", "56"], ["Turkey", "55"], ["The Netherlands", "52"], ["Brazil", "51"], ["Kenya", "50"], ["South Africa", "48"], ["Ireland", "48"], ["Denmark", "46"], ["Germany", "45"], ["Norway", "45"], ["Poland", "45"], ["Belgium", "45"], ["Canada", "44"], ["Switzerland", "44"], ["Austria", "40"], ["Croatia", "39"], ["Mexico", "39"], ["Sweden", "38"], ["Australia", "38"], ["Romania", "38"], ["Japan", "37"], ["Spain", "36"], ["Singapore", "36"], ["The Czech Republic", "33"], ["Argentina", "33"], ["Bulgaria", "33"], ["Chile", "30"], ["Hong Kong", "30"], ["Italy", "29"], ["United States", "29"], ["Greece", "28"], ["Slovakia", "28"], ["United Kingdom", "28"], ["Hungary", "27"], ["Philippines", "27"], ["Malaysia", "25"], ["Taiwan", "24"], ["France", "23"], ["South Korea", "21"]], "labels": {"name": "Country", "values": ["Finland", "Portugal", "Turkey", "The Netherlands", "Brazil", "Kenya", "South Africa", "Ireland", "Denmark", "Germany", "Norway", "Poland", "Belgium", "Canada", "Switzerland", "Austria", "Croatia", "Mexico", "Sweden", "Australia", "Romania", "Japan", "Spain", "Singapore", "The Czech Republic", "Argentina", "Bulgaria", "Chile", "Hong Kong", "Italy", "United States", "Greece", "Slovakia", "United Kingdom", "Hungary", "Philippines", "Malaysia", "Taiwan", "France", "South Korea"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2020-06/DNR_2020_FINAL.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2020", "title": "Share of Respondents That Agree They Can Trust the News Most of the Time", "topic": "Disinformation", "method": "Survey (N=79027)", "source": "Newman, Nick, Richard Fletcher, Anne Shulz, Simge Andi,and Rasmus Kleis Nielsen. \"Reuters Institute Digital News Report 2020,\" Reuters Institute, 2020 ", "sub_topic": "Trust in News Sources", "chart_number": "177", "geographical": "Global"}, "description": "The chart shows the percentage of respondents who agreed with the statement, \"I think you can trust the news most of the time.\" The results show that only in six out of 40 countries trust levels exceed 50%. The highlest levels of trust is found in Finland, with 56% agreeing with the statement, while South Korea exhibited the lowest levels of trust, only 21% agreement."},
{"data": [{"data": [84, 76, 76, 72, 67, 65, 65, 65, 65, 64, 63, 63, 63, 62, 62, 62, 60, 60, 60, 58, 57, 56, 55, 54, 54, 51, 51, 50, 49, 49, 46, 45, 45, 45, 42, 40, 37, 37, 35, 32], "name": "Percent Agreed"}], "_data": [["Country", "Percent Agreed"], ["Brazil", "84"], ["Portugal", "76"], ["Kenya", "76"], ["South Africa", "72"], ["United States", "67"], ["Singapore", "65"], ["Canada", "65"], ["Spain", "65"], ["Chile", "65"], ["Australia", "64"], ["United States", "63"], ["Greece", "63"], ["Maylasia", "63"], ["France", "62"], ["Ireland", "62"], ["Turkey", "62"], ["Argentina", "60"], ["South Korea", "60"], ["Mexico", "60"], ["Romania", "58"], ["Philippines", "57"], ["Finland", "56"], ["Croatia", "55"], ["Japan", "54"], ["Italy", "54"], ["Hong Kong", "51"], ["Bulgaria", "51"], ["Hungary", "50"], ["Belgium", "49"], ["Sweden", "49"], ["The Czech Republic", "46"], ["Taiwan", "45"], ["Switzerland", "45"], ["Poland", "45"], ["Norway", "42"], ["Austria", "40"], ["Denmark", "37"], ["Germany", "37"], ["Slovakia", "35"], ["The Netherlands", "32"]], "labels": {"name": "Country", "values": ["Brazil", "Portugal", "Kenya", "South Africa", "United States", "Singapore", "Canada", "Spain", "Chile", "Australia", "United States", "Greece", "Maylasia", "France", "Ireland", "Turkey", "Argentina", "South Korea", "Mexico", "Romania", "Philippines", "Finland", "Croatia", "Japan", "Italy", "Hong Kong", "Bulgaria", "Hungary", "Belgium", "Sweden", "The Czech Republic", "Taiwan", "Switzerland", "Poland", "Norway", "Austria", "Denmark", "Germany", "Slovakia", "The Netherlands"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2020-06/DNR_2020_FINAL.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2020", "title": "Share of Respondends Concerned About What is Real and What is Fake on the Internet When it Comes to Fake News", "topic": "Disinformation", "method": "Survey (N=79027)", "source": "Newman, Nick, Richard Fletcher, Anne Shulz, Simge Andi,and Rasmus Kleis Nielsen. \"Reuters Institute Digital News Report 2020,\" Reuters Institute, 2020 ", "sub_topic": "Trust in News Sources", "chart_number": "178", "geographical": "Global"}, "description": "The chart shows the percentage of persons surveyed who agreed with the statement, \"Thinking about online news, I am concerned about what is real and what is fake on the internet.\" The results show that more than half (56%) of the survey’s respondents across 40 countries remains concerned about what is real and fake on the internet when it comes to news. Brazilian citizens exhibited the highest levels of concern, with 84% agreeing with the statement; whereas respondents from the Netherlands displayed lower rates of concern at only 32%."},
{"data": [{"data": [85, null, 85, 82, 64, 64, null, null, 61, null, null, 87, null], "name": "2013"}, {"data": [93, 89, 88, 88, 87, 85, 82, 82, 80, 79, 77, 73, 88], "name": "2018"}], "_data": [["Country", "2013", "2018"], ["France", "85", "93"], ["Poland", "", "89"], ["Italy", "85", "88"], ["Belgium", "82", "88"], ["Germany", "64", "87"], ["The United Kingdom", "64", "85"], ["The Netherlands", "", "82"], ["Austria", "", "82"], ["Sweden", "61", "80"], ["Spain", "", "79"], ["Denmark", "", "77"], ["Hungary", "87", "73"], ["12 Country Average", "", "88"]], "labels": {"name": "Country", "values": ["France", "Poland", "Italy", "Belgium", "Germany", "The United Kingdom", "The Netherlands", "Austria", "Sweden", "Spain", "Denmark", "Hungary", "12 Country Average"]}, "metadata": {"link": "https://fra.europa.eu/en/publication/2018/experiences-and-perceptions-antisemitism-second-survey-discrimination-and-hate", "type": "Problem", "unit": "Per cent (%)", "year": "2013-2018", "title": "Perceptions of changes in the level of expressions of antisemitism on the internet in the country over the past five years, by EU Member State", "topic": "Hate Speech", "method": "Survey (N=16395)", "source": "European Union Agency for Fundamental Rights. Experiences and perceptions of antisemitism: Second survey on discrimination and hate crime against Jews in the EU (Luxembourg: Publications Office of the European Union, 2018)", "sub_topic": "Prevalence of hate speech", "chart_number": "179", "geographical": "European Union"}, "description": "This chart shows the difference in perceptions of changes in the level of antisemitism on the internet from 2013 to 2018. Respondents in both 2013 and 2018 were asked if \"over the past five years, has antisemitism on the internet, including on social media, increased, stayed the same or decreased.\" The amounts recorded show the percentage who answered \"increased a lot\" and increased a little.\" The most dramatic changes in perceptions occured in Germany (+23) and the United Kingdom (+21), and the only country who recorded a decrease in perceived antisemitism online was Hungary."},
{"data": [{"data": [81, 19, 14, 11, 7, 5, 5, 3, 12, 4, 2], "name": "Imports"}, {"data": [81, 10, 13, 11, 15, 14, 10, 10, 24, 3, 1], "name": "Exports"}], "_data": [["Countries", "Imports", "Exports"], ["European Union", "81", "81"], ["China", "19", "10"], ["United States", "14", "13"], ["India and South East Asia", "11", "11"], ["Middle East and North Africa", "7", "15"], ["Eastern Europe, Caucasus and Balkans", "5", "14"], ["Latin America", "5", "10"], ["Russia", "3", "10"], ["Other (Spontaneous)", "12", "24"], ["None (Spontaneous)", "4", "3"], ["Don't Know", "2", "1"]], "labels": {"name": "Countries", "values": ["European Union", "China", "United States", "India and South East Asia", "Middle East and North Africa", "Eastern Europe, Caucasus and Balkans", "Latin America", "Russia", "Other (Spontaneous)", "None (Spontaneous)", "Don't Know"]}, "metadata": {"link": "https://data.europa.eu/euodp/en/data/dataset/S2090_421_ENG", "type": "Problem", "unit": "Per cent (%)", "year": "2014", "title": "Sources and Destinations for European Small and Medium-sized Enterprises Imports and Exports", "topic": "Illegal Content", "method": "Survey (N=4320)", "source": "European Commision. Flash Barometer Report 421: Internationalisation of Small and Medium-sized Enterprises (Brussels: European Commission, 2015)", "sub_topic": "Market Growth", "chart_number": "180", "geographical": "European Union"}, "description": "This chart highlights that the overwhelming majority of European SMEs rely heavily on the European Single Market, with SMEs receiving 81% of their imports from and sending 81% of their exports to other EU Member States."},
{"data": [{"data": [78, 70.4, 69.7, 66.4, 66, 63.6, 62.4], "name": "Type of barrier"}], "_data": [["Obstacles", "Type of barrier"], ["Complex adminstrative procedures", "78"], ["Different national service rules", "70.4"], ["Inaccessibility to information on rules and requirements", "69.7"], ["Different national product rules", "66.4"], ["Different contractual/ legal practices", "66"], ["Concerns about reolving commercial or adminstrative disputes", "63.6"], ["Differing VAT procedure", "62.4"]], "labels": {"name": "Obstacles", "values": ["Complex adminstrative procedures", "Different national service rules", "Inaccessibility to information on rules and requirements", "Different national product rules", "Different contractual/ legal practices", "Concerns about reolving commercial or adminstrative disputes", "Differing VAT procedure"]}, "metadata": {"link": "https://www.eurochambres.eu/wp-content/uploads/2020/08/Business-Survey-The-state-of-the-Single-Market-Barriers-and-Solutions-DECEMBER-2019.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2019", "title": "Most Significant Obstacles Created by Market Fragmentation for European Small and Midsize Enterprises", "topic": "Illegal Content", "method": "Survey (N=1107)", "source": "Eurochambres. The State of the Single Market: Barriers and Solutions (Brussels: Eurochambers, 2019)", "sub_topic": "Market Growth", "chart_number": "181", "geographical": "European Union"}, "description": "This chart demonstrates that a clear majority of Europeans SMEs feel that failures in the single market have led to signifcant or very significant barriers in the expansion of their business. The most promiment of these barriers being complex administrative procedures and differing rules by individual Member States. "},
{"data": [{"data": [91.2, 86.5, 85, 83, 82.5, 81.6, 77.5], "name": "Type of solution"}], "_data": [["Solutions", "Type of solution"], ["Cutting red tape", "91.2"], ["Clearer information on single EU online portal about operating in another EU country", "86.5"], ["Adminstrative simplification for trading goods and services in other Member States.", "85"], ["Improved implementation of the EU law by more cooperation on enforcement", "83"], ["Take greater account of the impact of impact of new regulations on SMEs", "82.5"], ["Ensure better legal protection before national and EU authorities", "81.6"], ["Harmonisation of national regulations and standards", "77.5"]], "labels": {"name": "Solutions", "values": ["Cutting red tape", "Clearer information on single EU online portal about operating in another EU country", "Adminstrative simplification for trading goods and services in other Member States.", "Improved implementation of the EU law by more cooperation on enforcement", "Take greater account of the impact of impact of new regulations on SMEs", "Ensure better legal protection before national and EU authorities", "Harmonisation of national regulations and standards"]}, "metadata": {"link": "https://www.eurochambres.eu/wp-content/uploads/2020/08/Business-Survey-The-state-of-the-Single-Market-Barriers-and-Solutions-DECEMBER-2019.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2019", "title": "Percentage of European Businesses who Support Proposed Changes to the Single Market", "topic": "Illegal Content", "method": "Survey (N=1107)", "source": "Eurochambres. The State of the Single Market: Barriers and Solutions (Brussels: Eurochambers, 2019)", "sub_topic": "Market Growth", "chart_number": "182", "geographical": "European Union"}, "description": "This chart exhibits that an overwhelming majority of Europeans businesses support enacting reforms to the current single market structure in order to promote expansion and further development across sectors. The most favored reform was cutting redtape, including  but not limited to \"extensive reporting, information or documentation obligations.\" "},
{"data": [{"data": [12000000, 30000000, 2700000, 2000000], "name": "Amateurs"}, {"data": [1000000, 500000, 200000, 300000], "name": "Professionals"}], "_data": [["Social Media Platforms", "Amateurs", "Professionals"], ["YouTube", "12000000", "1000000"], ["Instagram", "30000000", "500000"], ["Twitch", "2700000", "200000"], ["Other", "2000000", "300000"]], "labels": {"name": "Social Media Platforms", "values": ["YouTube", "Instagram", "Twitch", "Other"]}, "metadata": {"link": "https://www.signalfire.com/blog/creator-economy/", "type": "Problem", "unit": "Number of persons", "year": "2020", "title": "Distribution of Content Creators on Social Media Platforms", "topic": "Illegal Content", "method": "Data collection", "source": "Yuanling Yuan, \"SignalFire’s Creator Economy Market Map,\" in SignalFire’s blog, October 2020", "sub_topic": "Platform Economy", "chart_number": "184", "geographical": "World"}, "description": "A recent report on the “creator economy” by Yuanling Yuan, senior associate at SignalFire, shows that there are over 50 million creators on Youtube, Instagram, Twitch, TikTok, and other social media platforms. The chart presents the distribution of these content creators by social media platforms and professional status, with approximately two million full-time creators that earn six figure salaries by creating content daily or weekly. And that massive distributed content creation engine means that about 90% of the video, audio, photo, and text-based content consumed today by Gen Z is created by individuals, not corporations.\n"},
{"data": [{"data": [47, null, 56, 16], "name": "Fairly / Very good"}, {"data": [34, null, 28, 38], "name": "Neither / Don't know"}, {"data": [19, null, 16, 46], "name": "Fairly / Very bad"}], "_data": [["Climate change", "Fairly / Very good", "Neither / Don't know", "Fairly / Very bad"], ["All survey's respondents", "47", "34", "19"], [" "], ["Respondents who think the climate change problem is serious", "56", "28", "16"], ["Respondents who think the climate change problem is not serious", "16", "38", "46"]], "labels": {"name": "Climate change", "values": ["All survey's respondents", " ", "Respondents who think the climate change problem is serious", "Respondents who think the climate change problem is not serious"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2020-06/DNR_2020_FINAL.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2020", "title": "Perceptions of the News Media Accuracy on Information Related to Climate Change", "topic": "Disinformation", "method": "Survey (N = 80155)", "source": "Newman, Nick, Richard Fletcher, Anne Shulz, Simge Andi,and Rasmus Kleis Nielsen. \"Reuters Institute Digital News Report 2020,\" Reuters Institute, 2020  ", "sub_topic": "Trust in Climate Change Information of the News Media", "chart_number": "185", "geographical": "Global"}, "description": "The chart presents the people perception of the accuracy of the information related climate change given by the media. The results are based on the responses of the survey question “To what extent do the news media do a good or bad job in giving me accurate information about climate change?” included in the “Reuters Institute Digital News Report 2020”. The report found that across markets around half (47%) say that the news media do a good job in providing accurate information about climate change. By contrast, the respondents saying the problem is not serious are far more likely to think the media are doing a bad job (46%) than a good job (16%). The seriousness of the climate change problem was assessed based on the answers to\nthe survey question “How serious a problem, if at all, do you think climate change is?” (55693 respondents considered the problem “Extremely serious or Very serious”, while 6794 considered “Not very serious or Not serious at all”).\n\n\n\n"},
{"data": [{"data": [96, 32], "name": "Mail/Express courier"}, {"data": [4, 24], "name": "Air"}, {"data": [0.1, 1], "name": "Sea/Vessel"}, {"data": [0.2, 43], "name": "Road/Vehicle"}], "_data": [["Topics", "Mail/Express courier", "Air", "Sea/Vessel", "Road/Vehicle"], ["Share in total number of customs seizures of counterfeit pharmaceuticals (worldwide)", "96", "4", "0.1", "0.2"], ["Share in the total seized value of counterfeit pharmaceuticals", "32", "24", "1", "43"]], "labels": {"name": "Topics", "values": ["Share in total number of customs seizures of counterfeit pharmaceuticals (worldwide)", "Share in the total seized value of counterfeit pharmaceuticals"]}, "metadata": {"link": "https://www.oecd-ilibrary.org/governance/trade-in-counterfeit-pharmaceutical-products_a7c7e054-en", "type": "Problem", "unit": "Per cent (%)", "year": "2014-2016", "title": "Distribution of Conveyance Methods for Counterfeit Pharmaceuticals (2014-2016)", "topic": "Illegal Products", "method": "Data collection", "source": "OECD/EUIPO. “Trade in Counterfeit Pharmaceutical Products,” Illicit Trade (Paris and Alicante: OECD Publishing and European Union Intellectual Property Office, 2020)", "sub_topic": "Counterfeit pharmaceuticals", "chart_number": "188", "geographical": "Global"}, "description": "The chart presents the distribution of transport modes for counterfeit pharmaceutical, based on the data from the OECD/EUIPO study on illicit trade in fake pharmaceuticals, published in 2020. The report builds on previous analyses of illicit trade, focusing on trade of counterfeit pharmaceuticals. In addition to possible economic damages for the sector, fake pharmaceuticals pose significant health risks, as often they are not properly formulated and may contain dangerous ingredients. The data highlighted that mail and courier services are the main modes of transport for counterfeit pharmaceuticals traded worldwide. In terms of volume, air is also an important means of transport in the global trade of fake pharmaceuticals. In terms of value, the main transport mode was by road transport and mail and postal services."},
{"data": [{"data": [null, null, 1, null, null, null, 1, null, null, 1, null, 2, 1, 1, 1, 5, 4, 1, 5, 4, 8, 12, 19, 24, 26, 45], "name": "Number of Publications"}], "_data": [["Year", "Number of Publications"], ["1990", "0"], ["1991", "0"], ["1992", "1"], ["1993", "0"], ["1994", "0"], ["1995", "0"], ["1996", "1"], ["1997", "0"], ["1998", "0"], ["1999", "1"], ["2000", "0"], ["2001", "2"], ["2002", "1"], ["2003", "1"], ["2004", "1"], ["2005", "5"], ["2006", "4"], ["2007", "1"], ["2008", "5"], ["2009", "4"], ["2010", "8"], ["2011", "12"], ["2012", "19"], ["2013", "24"], ["2014", "26"], ["2015", "45"]], "labels": {"name": "Year", "values": ["1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015"]}, "metadata": {"link": "https://reader.elsevier.com/reader/sd/pii/S0959652617317821?token=34FCBA25E407551DCD01B6228D749E1E310AB9FD08995792DC5AA93438734B888A893DF178E66D82FFFE152C9B708DDC", "type": "Problem", "unit": "Number of Publications", "year": "2017", "title": "Number of Publications Found in Literature Review on Science Denial by Publication Year (1990-2015)", "topic": "Disinformation", "method": "Data mining", "source": "Björnberg, Karin, Mikael Karlsson, Michael Gilek, Sven Hansson. \"Climate and Environmental Science Denial: A Review of the Scientific Literature Published in 1990-2015,\" Journal of Cleaner Production, 2017", "sub_topic": "Climate change and environmental science denial", "chart_number": "189", "geographical": "Global"}, "description": "The chart presents the annual trend of the number of reviewed publications on climate and environmental science denial from 1990 to 2015, based on the results from the report \"Climate and environmental science denial: A review of the scientificliterature published in 1990-2015.\" The data shows that there has been a steady increase in publications on climate and environmental science denial since 2010. In general, scientific denialism is the rejection of basic facts and concepts that are undisputed, well-supported parts of the scientific consensus on a subject, in favor of radical and controversial ideas. Specifically on climate science denial, a substantial body of scientific literature exists.  "},
{"data": [{"data": [58.2, 7.9], "name": "Mail"}, {"data": [23.8, 70.5], "name": "Vehicles"}, {"data": [9.5, 5], "name": "Air"}, {"data": [2, 15], "name": "Vessels"}, {"data": [6.5, 1.6], "name": "Other"}], "_data": [["Topics", "Mail", "Vehicles", "Air", "Vessels", "Other"], ["Share in total number of customs seizures", "58.2", "23.8", "9.5", "2", "6.5"], ["Share in the total number of pieces seized", "7.9", "70.5", "5", "15", "1.6"]], "labels": {"name": "Topics", "values": ["Share in total number of customs seizures", "Share in the total number of pieces seized"]}, "metadata": {"link": "http://www.wcoomd.org/-/media/wco/public/global/pdf/topics/enforcement-and-compliance/activities-and-programmes/illicit-trade-report/itr_2019_en.pdf?db=web", "type": "Problem", "unit": "Per cent (%)", "year": "2018-2019", "title": "Distribution of Conveyance Methods for Weapons and Ammunition, 2018-2019", "topic": "Illegal Products", "method": "Data collection", "source": "World Customs Organization. Illicit Trade Report (Brussels: World Customs Organization, 2019)", "sub_topic": "Weapons and ammunition", "chart_number": "190", "geographical": "Global"}, "description": "The chart presents the distribution of transport modes for weapons and ammunition seized by customs in 2019, based on the data from World Customs Organization's \"Illicit trade Report 2019.\" The report shows that air, mail, vehicle and vessel are the conveyance methods present in all the seizures of weapons and ammunition. While seizures of weapons and ammunition made from the mail represented 58.2% (6 089) of all seizures made in 2019 (10 469), their number of pieces accounted only for 7.9% (78 131) of the total number of pieces seized by customs. The majority (70.5%) of pieces seized were made via vehicle transport, and out of those 94.4% were ammunition. "},
{"data": [{"data": [6, null, 14, 13, 9, 9, 8, 7, 7, 7, 6, 5, 7, 6, 6, 5, 5, 6, 4, 5, 5, 4, 3, 3, 3, 1, 2, 2, null, 12, 4, null, 11, 9], "name": "Own website or apps"}, {"data": [1, null, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, null, 1, 1, 1, 1, null, 1, null, null, 1, 1, 1, null, 1, null, null, null, 1, 2, null, 1, null], "name": "Marketplace"}], "_data": [["2019", "Own website or apps", "Marketplace"], ["European Union", "6", "1"], [" "], ["Belgium", "14", "1"], ["Ireland", "13", "1"], ["Sweden", "9", "1"], ["United Kingdom", "9", "1"], ["Czechia", "8", "1"], ["Denmark", "7", "1"], ["France", "7", "1"], ["Netherlands", "7", "1"], ["Spain", "6", "1"], ["Lithuania", "5", "2"], ["Hungary", "7", "0"], ["Poland", "6", "1"], ["Portugal", "6", "1"], ["Malta", "5", "1"], ["Slovakia", "5", "1"], ["Finland", "6", "0"], ["Germany", "4", "1"], ["Estonia", "5", "0"], ["Croatia", "5", "0"], ["Cyprus", "4", "1"], ["Austria", "3", "1"], ["Romania", "3", "1"], ["Greece", "3", "0"], ["Bulgaria", "1", "1"], ["Latvia", "2", "0"], ["Slovenia", "2", "0"], [" "], ["Norway", "12", "1"], ["Iceland", "4", "2"], [" "], ["Serbia", "11", "1"], ["Bosnia and Herzegovina", "9", "0"]], "labels": {"name": "2019", "values": ["European Union", " ", "Belgium", "Ireland", "Sweden", "United Kingdom", "Czechia", "Denmark", "France", "Netherlands", "Spain", "Lithuania", "Hungary", "Poland", "Portugal", "Malta", "Slovakia", "Finland", "Germany", "Estonia", "Croatia", "Cyprus", "Austria", "Romania", "Greece", "Bulgaria", "Latvia", "Slovenia", " ", "Norway", "Iceland", " ", "Serbia", "Bosnia and Herzegovina"]}, "metadata": {"link": "https://ec.europa.eu/eurostat/databrowser/view/ISOC_EC_EVALN2__custom_410813/settings_1/table?lang=en", "type": "Problem", "unit": "Per cent of total turnover (%)", "year": "2019", "title": "Turnover From Web Sales Broken Down by Own Website or Apps and Marketplace, 2019", "topic": "Illegal Products", "method": "Data collection", "source": "Eurostat. Value of e-commerce sales (table code: ISOC_EC_EVALN2), online data, last accessed 04 January 2021", "sub_topic": "Web sales", "chart_number": "191", "geographical": "Europe"}, "description": "The chart presents the share of e-commerce sales in total turnover, in 2019 for European enterprises (with more than 10 employees), by own website or apps and marketplace. Web sales via marketplaces for Hungary, Finland, Estonia, Croatia, Greece, Latvia, Slovakia and Bosnia and Herzegovina are less 1% and, therefore, not visible on the chart.  The data are not available for Luxemboug (confidential), Italy (unreliable), Montenegro (unreliable) and North Macedonia (no data)."},
{"data": [{"data": [37, 25], "name": "Share of enterprises"}], "_data": [["Categories", "Share of enterprises"], ["Large Enterprises (250 persons employed or more)", "37"], ["SMEs (10-249 persons employed)", "25"]], "labels": {"name": "Categories", "values": ["Large Enterprises (250 persons employed or more)", "SMEs (10-249 persons employed)"]}, "metadata": {"link": "https://ec.europa.eu/eurostat/databrowser/view/isoc_cismt/default/table?lang=en", "type": "Problem", "unit": "Per cent (%)", "year": "2018", "title": "Share of Enterprises Who Pay to Advertise Online, 2018", "topic": "Illegal Content", "method": "Data collection", "source": "Eurostat. Social media use by type, internet advertising, online data, last accessed 04 January 2021", "sub_topic": "Advertising online", "chart_number": "192", "geographical": "European Union"}, "description": "The chart presents the share of entreprises that pay for on-line advertising in European Union in 2018, by size of enterprise (the chart does not include the enterprises from the financial sector)."},
{"data": [{"data": [7.6, 11.5, 15.2, 4.6], "name": "Post/Express"}, {"data": [67, 64.4, 54.3, 71.1], "name": "Sea"}, {"data": [25.4, 24.1, 30.5, 24.4], "name": "Other methods"}], "_data": [["Year", "Post/Express", "Sea", "Other methods"], ["2016", "7.60%", "67.00%", "25.40%"], ["2017", "11.50%", "64.40%", "24.10%"], ["2018", "15.20%", "54.30%", "30.50%"], ["2019", "4.60%", "71.10%", "24.40%"]], "labels": {"name": "Year", "values": ["2016", "2017", "2018", "2019"]}, "metadata": {"link": "https://ec.europa.eu/taxation_customs/sites/taxation/files/ipr_report_2020.5464_en_04.pdf", "type": "Problem", "unit": null, "year": "2016-2019", "title": "Distribution of Items Infringing Intellectual Property Rights Detained at the Custom Border in the European Union, by Conveyance Method (2016-2019)", "topic": "Illegal Products", "method": "Data collection", "source": "European Commission. Report on the European Union Customs Enforcement of Intellectual Property Rights: Results at the EU Border, 2019  (Luxembourg: Publication Office of the European Union, 2020)", "sub_topic": "Counterfeit and pirated goods", "chart_number": "198", "geographical": "European Union"}, "description": "The chart presents the distribution of goods (by the number of articles) infringing Intellectual Property Rights detentained at the European Union borders, by different conveyance method used, between 2016-2019. The results show that more than half of the articles detained were delivered by sea, while postal deliveries by small parcels remain relatively limited, registering a significant decline in 2019 (over 10%). The other methods of conveyance used include air, road, rail and inland waterways. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020.  "},
{"data": [{"data": [73.6, 75.9, 83.2, 84.8], "name": "Post/Express"}, {"data": [2.9, 2.8, 2.2, 1.9], "name": "Sea"}, {"data": [23.5, 21.2, 14.5, 13.3], "name": "Other methods"}], "_data": [["Year", "Post/Express", "Sea", "Other methods"], ["2016", "73.60%", "2.90%", "23.50%"], ["2017", "75.90%", "2.80%", "21.20%"], ["2018", "83.20%", "2.20%", "14.50%"], ["2019", "84.80%", "1.90%", "13.30%"]], "labels": {"name": "Year", "values": ["2016", "2017", "2018", "2019"]}, "metadata": {"link": "https://ec.europa.eu/taxation_customs/sites/taxation/files/ipr_report_2020.5464_en_04.pdf", "type": "Problem", "unit": "Per cent (%)", "year": "2016-2019", "title": "Distribution of Registered Cases of Custom Seizures at the European Union Borders, by Conveyance Method (2016-2019)", "topic": "Illegal Products", "method": "Data collection", "source": "European Commission. Report on the European Union Customs Enforcement of Intellectual Property Rights: Results at the EU Border, 2019  (Luxembourg: Publication Office of the European Union, 2020)", "sub_topic": "Counterfeit and pirated goods", "chart_number": "199", "geographical": "European Union"}, "description": "The chart presents the distribution of registered cases in total custom seizures at the European Union borders, by different conveyance method used, between 2016-2019. The results show that more than half of the registered cases involved postal delivered goods, while sea delivery method account for less than 3%. Other methods of conveyance include air, road, rail and inland waterways. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020.  "},
{"data": [{"data": [87.36, 96.42, 99.75], "name": "Based on governmental notices provided to the established single contact points"}, {"data": [92.04, 93.56, 97.23], "name": "Found through the monitoring of public recall websites, such as the EU Safety Gate"}], "_data": [["Time Period", "Based on governmental notices provided to the established single contact points", "Found through the monitoring of public recall websites, such as the EU Safety Gate"], ["October 2018 - March 2019", "87.36", "92.04"], ["April 2019 - September 2019", "96.42", "93.56"], ["October 2019 - May 2020", "99.75", "97.23"]], "labels": {"name": "Time Period", "values": ["October 2018 - March 2019", "April 2019 - September 2019", "October 2019 - May 2020"]}, "metadata": {"link": "https://ec.europa.eu/info/business-economy-euro/product-safety-and-requirements/product-safety/product-safety-pledge_en", "type": "Solution", "unit": "Per cent (%)", "year": "2018-2020", "Range": "0 100", "title": "Share of Identified Product Listings Removed Within Two Working Days (2018-2020)", "topic": "Illegal Products", "method": "Data collection", "source": "European Commission. Progress Reports on the Implementation of the Product Safety Pledge, 2018-2021", "sub_topic": "Counterfeit and pirated goods", "chart_number": "206", "geographical": "European Union"}, "description": "The chart presents the key performance indicators for monitoring the implementation of the Product Safety Pledge, over the period 2018 - 2020. The KPIs for the periods represent the arithmetic average of the percentages provided by the four signatories, they do not represent the weighted average according to the number of products reported. The method is valid for periods October 2018 - March 2019 and April 2019 - September 2019. For the period October 2019 - May 2020, a different method of calculus was used for KPIs, making the latest values not comparable with the previous values reported. "},
{"data": [{"data": [87.36, 96.42, 99.75, 87.51, 75.4, 79.1, 98.1], "name": "Based on governmental notices provided to the established single contact points"}, {"data": [92.04, 93.56, 97.23, 98.83, 97.13, 97.2, 98.2], "name": "Found through the monitoring of public recall websites, such as the EU Safety Gate"}], "_data": [["Time Period", "Based on governmental notices provided to the established single contact points", "Found through the monitoring of public recall websites, such as the EU Safety Gate"], ["October 2018 - March 2019", "87.36", "92.04"], ["April 2019 - September 2019", "96.42", "93.56"], ["October 2019 - May 2020", "99.75", "97.23"], ["June 2020 - November 2020", "87.51", "98.83"], ["December 2020 - May 2021", "75.4", "97.13"], ["June 2021 - November 2021", "79.1", "97.2"], ["December 2021 - May 2022", "98.1", "98.2"]], "labels": {"name": "Time Period", "values": ["October 2018 - March 2019", "April 2019 - September 2019", "October 2019 - May 2020", "June 2020 - November 2020", "December 2020 - May 2021", "June 2021 - November 2021", "December 2021 - May 2022"]}, "metadata": {"link": "https://ec.europa.eu/info/business-economy-euro/product-safety-and-requirements/product-safety/product-safety-pledge_en", "type": "Solution", "unit": "Per cent (%)", "year": "2018-2022", "Range": "0 100", "title": "Share of Identified Product Listings Removed Within Two Working Days (2018-2022)", "topic": "Illegal Products", "method": "Data collection", "source": "European Commission. Progress Reports on the Implementation of the Product Safety Pledge, 2018-2022", "sub_topic": "Counterfeit and pirated goods", "chart_number": "206.1", "geographical": "European Union"}, "description": "The chart presents the key performance indicators for monitoring the implementation of the Product Safety Pledge, over the period October 2018 - May 2022. The KPIs for the periods represent the arithmetic average of the percentages provided by the four signatories, they do not represent the weighted average according to the number of products reported. The method is valid for periods October 2018 - March 2019 and April 2019 - September 2019. For the period October 2019 - May 2020, a different method of calculus was used for KPIs, making the latest values not comparable with the previous values reported. From June 2020 onwards, for the following reportings, the signatories of the Product Safety Pledge provide absolute numbers of the dangerous products identified and removed to facilitate easier monitoring and comparability for future reports."},
{"data": [{"data": [86.3, 97.4, 98.2], "name": "Proactively by the Internet Platform"}, {"data": [13.7, 2.65, 1.8], "name": "As a result of notices sent by Rights Owners"}], "_data": [["Time Period", "Proactively by the Internet Platform", "As a result of notices sent by Rights Owners"], ["November - December 2016", "86.3", "13.7"], ["May - June 2017", "97.4", "2.65"], ["May - June 2019", "98.2", "1.8"]], "labels": {"name": "Time Period", "values": ["November - December 2016", "May - June 2017", "May - June 2019"]}, "metadata": {"link": "https://ec.europa.eu/growth/industry/policy/intellectual-property/enforcement/memorandum-understanding-sale-counterfeit-goods-internet_en", "type": "Solution", "unit": "Per cent (%)", "year": "2016-2019", "title": "Amount of Listings Removed as a Result of an Alleged Infringement of the Right Owners' IPR (2016-2019)", "topic": "Illegal Products", "method": "Data collection", "source": "European Commission. Commission Staff Working Document: Report on the Functioning of the Memorandum of Understanding on the Sale of Counterfeit Goods on the Internet (Brussels: European Commission, 2020)", "sub_topic": "Counterfeit and pirated goods", "chart_number": "208", "geographical": "European Union"}, "description": "The chart presents the key performance indicators for monitoring the implementation of the Memorandum of Understanding on Sale of Counterfeit Goods, over the period 2016 - 2019. The results of the report show that the platforms’ pro-active measures are the main driver for the amount of listings removed, as they accounted for 98% of the listings removed in 2019 (12% increase since 2016)."},
{"data": [{"data": [23.3, 9.6, 6, 2.3, 4.2, 1.6, 0.9, 1, 0.6, 0.5, 0.1], "name": "Direct Lost Sales (EUR billion)"}], "_data": [["Sector", "Direct Lost Sales (EUR billion)"], ["Clothing, Footwear and Accessories", "23.3"], ["Cosmetics & Personal care", "9.6"], ["Pharmaceuticals", "6"], ["Spirits & Wine", "2.3"], ["Smartphones (refers to 2015 only)", "4.2"], ["Jewellery & Watches", "1.6"], ["Handbags & Luggage", "0.9"], ["Toys & Games", "1"], ["Sports Goods", "0.6"], ["Pesticides & Agrochemicals", "0.5"], ["Recorded Music", "0.1"]], "labels": {"name": "Sector", "values": ["Clothing, Footwear and Accessories", "Cosmetics & Personal care", "Pharmaceuticals", "Spirits & Wine", "Smartphones (refers to 2015 only)", "Jewellery & Watches", "Handbags & Luggage", "Toys & Games", "Sports Goods", "Pesticides & Agrochemicals", "Recorded Music"]}, "metadata": {"link": "https://op.europa.eu/s/oDKI", "type": "Problem", "unit": "Billion EUR", "year": "2013-2017", "title": "Estimated Value of Direct Lost Sales Due To Infringement in Selected IPR-Intensive Industries in the European Union, 2013-2017", "topic": "Illegal Products", "method": "Data collection", "source": "European Union Intellectual Property Office. 2020 Status Report on IPR Infringement: Why IP Rights Are Important, IPR Infringement, and the Fight Against Counterfeiting and Piracy (Alicante: EUIPO, 2020)", "sub_topic": "Counterfeit and pirated goods", "chart_number": "211", "geographical": "European Union"}, "description": "The chart presents on overview of estimated direct economic costs of infringement in selected IPR-intensive industries in the European Union, over the period 2013-2017. The results of the EUIPO report show that counterfeiting and piracy significantly impact the sales in the clothing, footware and accessories industries, accounting for 46% of their direct lost sales. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020.  "},
{"data": [{"data": [263196, 99963, 20040, 5681, 12146, 6715, 3930, 3286, 767, 280], "name": "Direct Employment Loss"}], "_data": [["Sector", "Direct Employment Loss"], ["Clothing, Footwear and Accessories", "263196"], ["Cosmetics & Personal care", "99963"], ["Pharmaceuticals", "20040"], ["Spirits & Wine", "5681"], ["Jewellery & Watches", "12146"], ["Handbags & Luggage", "6715"], ["Toys & Games", "3930"], ["Sports Goods", "3286"], ["Pesticides & Agrochemicals", "767"], ["Recorded Music", "280"]], "labels": {"name": "Sector", "values": ["Clothing, Footwear and Accessories", "Cosmetics & Personal care", "Pharmaceuticals", "Spirits & Wine", "Jewellery & Watches", "Handbags & Luggage", "Toys & Games", "Sports Goods", "Pesticides & Agrochemicals", "Recorded Music"]}, "metadata": {"link": "https://op.europa.eu/s/oDKI", "type": "Problem", "unit": "Number of jobs", "year": "2013-2017", "title": "Direct Employment Loss Due To Infringement in Selected IPR-Intensive Industries in the European Union (2013-2017)", "topic": "Illegal Products", "method": "Data collection", "source": "European Union Intellectual Property Office. 2020 Status Report on IPR Infringement: Why IP Rights Are Important, IPR Infringement, and the Fight Against Counterfeiting and Piracy (Alicante: EUIPO, 2020)", "sub_topic": "Counterfeit and pirated goods", "chart_number": "212", "geographical": "European Union"}, "description": "The chart presents on overview of estimated direct economic costs of infringement in selected IPR-intensive industries in the European Union, over the period 2013-2017. The results of the EUIPO report show that counterfeiting and piracy significantly impact the clothing, footware and accessories industries, accounting for 63% of direct employment losses in this area. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020.  "},
{"data": [{"data": [51.7, 9.5, 6.1, 4, 4, 2.6, 2.6, 2.6, 16.9], "name": "Share in total accounts"}], "_data": [["Number of days", "Share in total accounts"], ["0 - 50 days", "51.70%"], ["51 - 100 days", "9.50%"], ["101-150 days", "6.10%"], ["151-200 days", "4.00%"], ["201-250 days", "4.00%"], ["251-300 days", "2.60%"], ["301-350 days", "2.60%"], ["351-400 days", "2.60%"], ["Over 401 days", "16.90%"]], "labels": {"name": "Number of days", "values": ["0 - 50 days", "51 - 100 days", "101-150 days", "151-200 days", "201-250 days", "251-300 days", "301-350 days", "351-400 days", "Over 401 days"]}, "metadata": {"link": "https://extremism.gwu.edu/sites/g/files/zaxdzs2191/f/DigitalDecayFinal_0.pdf", "type": "Solution", "unit": "Per cent (%)", "year": "2016-2017", "title": "Duration of Account Activity of English-Language Pro-Islamic State Accounts on Twitter, 2016-2017", "topic": "Incitement to Terrorism", "method": "Data collection", "source": "Alexander, Audrey. DIGITAL DECAY? Tracing Change Over Time Among English-Language Islamic State Sympathizers on Twitter (Washington: Programme on Extremism, The George Washington University, 2017)", "sub_topic": "Social Media Accounts", "chart_number": "215", "geographical": "Global"}, "description": "The chart shows the distribution of the duration of Twitter accounts of English-language of Islamic State sympathizers. The report collected and reviewed 845646 tweets produced by 1782 English-language pro-Islamic State accounts from 15 February 2016 to 01 May 2017. In the context of the study, a user’s \"duration of activity\" is quantified by the number of days between an account’s first and last tweet. "},
{"data": [{"data": [141, 108, 82], "name": "Number of followers"}], "_data": [["Time period", "Number of followers"], ["15 February 2016 to 11 July 2016", "141"], ["11 July 2016 to 05 December 2016", "108"], ["05 December 2016 to 01 May 2017", "82"]], "labels": {"name": "Time period", "values": ["15 February 2016 to 11 July 2016", "11 July 2016 to 05 December 2016", "05 December 2016 to 01 May 2017"]}, "metadata": {"link": "https://extremism.gwu.edu/sites/g/files/zaxdzs2191/f/DigitalDecayFinal_0.pdf", "type": "Solution", "unit": "Value", "year": "2016-2017", "title": "Median Number of Followers, After Accounts’ Suspension of English-Language pro-Islamic State Sympathizers, 2016-2017", "topic": "Incitement to Terrorism", "method": "Data collection", "source": "Alexander, Audrey. DIGITAL DECAY? Tracing Change Over Time Among English-Language Islamic State Sympathizers on Twitter (Washington: Programme on Extremism, The George Washington University, 2017)", "sub_topic": "Social Media Accounts", "chart_number": "216", "geographical": "Global"}, "description": "The chart shows the median number of followers of Twitter accounts of English-language of Islamic State sympathizers, after accounts' suspension by the platform. The results of study shows that the account’s suspension impacts the efforts to rebuild a robust followership for the English-language pro-Islamic State sympatizers after suspension."},
{"data": [{"data": [0.12, 0.19, 0.17, 0.2, 0.22, 0.27, 0.25, 0.23, 0.31, 0.39, 0.35, 0.4, 0.49, 0.51, 0.44, 0.41, 0.52, 0.55, 0.47, 0.42, 0.03, 0.02, 0.02, 0.02, 0.02, 0.01], "name": "Number of posts"}], "_data": [["Time period", "Number of posts"], ["April 2018", "0.12"], ["May 2018", "0.19"], ["June 2018", "0.17"], ["July 2018", "0.2"], ["August 2018", "0.22"], ["September 2018", "0.27"], ["October 2018", "0.25"], ["November 2018", "0.23"], ["December 2018", "0.31"], ["January 2019", "0.39"], ["February 2019", "0.35"], ["March 2019", "0.4"], ["April 2019", "0.49"], ["May 2019", "0.51"], ["June 2019", "0.44"], ["July 2019", "0.41"], ["August 2019", "0.52"], ["September 2019", "0.55"], ["October 2019", "0.47"], ["November 2019", "0.42"], ["December 2019", "0.03"], ["January 2020", "0.02"], ["February 2020", "0.02"], ["March 2020", "0.02"], ["April 2020", "0.02"], ["May 2020", "0.01"]], "labels": {"name": "Time period", "values": ["April 2018", "May 2018", "June 2018", "July 2018", "August 2018", "September 2018", "October 2018", "November 2018", "December 2018", "January 2019", "February 2019", "March 2019", "April 2019", "May 2019", "June 2019", "July 2019", "August 2019", "September 2019", "October 2019", "November 2019", "December 2019", "January 2020", "February 2020", "March 2020", "April 2020", "May 2020"]}, "metadata": {"link": "https://crestresearch.ac.uk/resources/how-telegram-disruption-impacts-jihadist-platform-migration/", "type": "Solution", "unit": "Million", "year": "2018-2020", "title": "Number of Posts on Telegram, Six Months Before and Six Months After Europol's Interventions, 2018-2020", "topic": "Incitement to Terrorism", "method": "Data collection", "source": "Amarasingam, Amarnath, Shiraz Maher, Charlie Winter. How Telegram Disruption Impacts Jihadist Platform Migration (London: Centre for Research and Evidence on Security Threats (CREST), 2021)", "sub_topic": "Social Media Accounts", "chart_number": "217", "geographical": "Global"}, "description": "The chart shows the evolution of the number of organic and forwarded posts on Telegram platform, after the two interventions of Europol in October 2018 and November 2019. The results of study shows that the 2019 intervention had a profound impact on the number of posts on Telegram and this impact was not short-term or temporary."},
{"data": [{"data": [10577, 259807, 53709, 470610], "name": "30 days before the Action Day"}, {"data": [8937, 234568, 18519, 24969], "name": "30 days after the Action Day"}], "_data": [["Number of posts", "30 days before the Action Day", "30 days after the Action Day"], ["Organic posts, 2018", "10577", "8937", "", "-0.1550534178"], ["Forwarded posts, 2018", "259807", "234568", "", "-0.09714518854"], ["Organic posts, 2019", "53709", "18519", "", "-0.6551974529"], ["Forwarded posts, 2019", "470610", "24969", "", "-0.9469433289"]], "labels": {"name": "Number of posts", "values": ["Organic posts, 2018", "Forwarded posts, 2018", "Organic posts, 2019", "Forwarded posts, 2019"]}, "metadata": {"link": "https://crestresearch.ac.uk/resources/how-telegram-disruption-impacts-jihadist-platform-migration/", "type": "Solution", "unit": "Values", "year": "2018-2020", "title": "Number of Posts on Telegram, 30 Days Before and 30 Days After Europol's Interventions, 2018-2020", "topic": "Incitement to Terrorism", "method": "Data collection", "source": "Amarasingam, Amarnath, Shiraz Maher, Charlie Winter. How Telegram Disruption Impacts Jihadist Platform Migration (London: Centre for Research and Evidence on Security Threats (CREST), 2021)", "sub_topic": "Social Media Accounts", "chart_number": "218", "geographical": "Global"}, "description": "The chart shows the evolution of the number of organic and forwarded posts on Telegram platform, after the two interventions of Europol in October 2018 and November 2019. The results of study shows that the 2019 intervention had a profound impact on the number of posts on Telegram compared to the 2018 intervention. While the 2018 intervention showed only 15% decline in organic posts and 9.7% decline in forwarded ones, the 2019 one resulted in 95.5% decline in organic posts and 95% decline in forwarded ones. "},
{"data": [{"data": [677, 472, 244, 146, 144], "name": "Per cent of change"}], "_data": [["Apps", "Per cent of change"], ["Signal", "677"], ["CloutHub", "472"], ["MeWe", "244"], ["Telegram", "146"], ["Rumble", "144"]], "labels": {"name": "Apps", "values": ["Signal", "CloutHub", "MeWe", "Telegram", "Rumble"]}, "metadata": {"link": "https://www.axios.com/the-online-far-right-is-moving-underground-e429d45d-1b30-46e0-82a3-6e240bf44fef.html", "type": "Problem", "unit": "Per cent (%)", "year": "2021", "title": "Change in social app downloads in United States in January 2021", "topic": "Incitement to Terrorism", "method": "Data collection", "source": "Daly, Kyle, Sara Fischer. \"The online far right is moving underground,\" article published in Axios Technology, 13 January 2021 ", "sub_topic": "Social media accounts", "chart_number": "219", "geographical": "Global"}, "description": "The chart shows the per cent of change in the downloads of social apps in United States, from 05 to 10 January 2021. The article presents a short analysis of motives, trends and possible effects of policy changes of major social network platforms such as Twitter, Facebook and Whatsapp."},
{"data": [{"data": [23.1, 17, 13.5, 12.5, 6, 5, 3.3, 2.7, 2, 1.7], "name": "2016"}, {"data": [21.3, 17.8, 16, 13.9, 5.9, 3.5, 3.2, 3.9, 1.6, 2.8], "name": "2013"}], "_data": [["Products", "2016", "2013"], ["Footware", "23.1", "21.3"], ["Clothing, knitted or crocheted", "17", "17.8"], ["Articles of leather", "13.5", "16"], ["Electrical machinery and equipment", "12.5", "13.9"], ["Watches", "6", "5.9"], ["Optical, photographic and medical instruments", "5", "3.5"], ["Perfumery and cosmetics", "3.3", "3.2"], ["Toys", "2.7", "3.9"], ["Jewellery", "2", "1.6"], ["Pharmaceutical products", "1.7", "2.8"]], "labels": {"name": "Products", "values": ["Footware", "Clothing, knitted or crocheted", "Articles of leather", "Electrical machinery and equipment", "Watches", "Optical, photographic and medical instruments", "Perfumery and cosmetics", "Toys", "Jewellery", "Pharmaceutical products"]}, "metadata": {"link": "https://doi.org/10.1787/g2g9f533-en", "type": "Problem", "unit": "Per cent (%)", "year": "2013, 2016", "title": "Product Categories Most Subject to Counterfeiting and Piracy Goods, 2013 and 2016", "topic": "Illegal Products", "method": "Data collection", "source": "OECD, EUIPO. Illicit Trade: Trends in Trade in Counterfeit and Pirated Goods (Paris and Alicante: OECD Publishing and European Union Intellectual Property Office, 2019)", "sub_topic": "Counterfeit and pirated goods", "chart_number": "220", "geographical": "Global"}, "description": "The chart shows the distribution of the product categories most subject to counterfeiting and piracy, in 2013 and 2016, based on the results of the OECD-EUIPO report on illegal trade. The report shows that product categories \"Footware\", \"Clothing, knitted or crocheted\" and \"Articles of leather\" have the highest propensity of being subject to counterfeit and piracy."},
{"data": [{"data": [36.9, 19.4], "name": "Air"}, {"data": [36.2, 65.3], "name": "Vehicles"}, {"data": [26.9, 15.3], "name": "Other"}], "_data": [["Topics", "Air", "Vehicles", "Other"], ["Share in total number of customs seizures", "36.9", "36.2", "26.9"], ["Share in the total number of items seized", "19.4", "65.3", "15.3"]], "labels": {"name": "Topics", "values": ["Share in total number of customs seizures", "Share in the total number of items seized"]}, "metadata": {"link": "http://www.wcoomd.org/-/media/wco/public/global/pdf/topics/enforcement-and-compliance/activities-and-programmes/illicit-trade-report/itr_2019_en.pdf?db=web", "type": "Problem", "unit": "Per cent (%)", "year": "2019", "title": "Distribution of Conveyance Methods for Cultural Artefacts in Illegal Trade, 2019", "topic": "Illegal Products", "method": "Data collection", "source": "World Customs Organization. Illicit Trade Report (Brussels: World Customs Organization, 2019)", "sub_topic": "Cultural heritage", "chart_number": "221", "geographical": "Global"}, "description": "The chart presents the distribution of transport modes for cultural artefacts seized by customs in 2019, based on the data from World Customs Organization's \"Illicit trade Report 2019.\" The report shows that air and vehicle are the main conveyance methods for cultural artefacts, representing 73% in total seizures. In 2019, out of 217 seizures, 100 were air seizures (36.9%) and 98 vehicles seizures (36.2%). However, by number of artefacts seized, the vehicle seizures accounted for 65% (6 138), while air seizures only for 19% (1 826) of the total number of artefacts seized by customs."},
{"data": [{"data": [54.7, 85.7], "name": "Currency and medals"}, {"data": [22.2, 4.7], "name": "Fauna, flora, minerals, anatomy, and fossils"}, {"data": [23.1, 9.6], "name": "Others"}], "_data": [["Years", "Currency and medals", "Fauna, flora, minerals, anatomy, and fossils", "Others"], ["2019", "54.7", "22.2", "23.1"], ["2018", "85.7", "4.7", "9.6"]], "labels": {"name": "Years", "values": ["2019", "2018"]}, "metadata": {"link": "http://www.wcoomd.org/-/media/wco/public/global/pdf/topics/enforcement-and-compliance/activities-and-programmes/illicit-trade-report/itr_2019_en.pdf?db=web", "type": "Problem", "unit": "Per cent (%)", "year": "2018-2019", "title": "Distribution of Share of Cultural Artefacts in Illegal Trade, by Selected Categories of Artefacts (2018-2019)", "topic": "Illegal Products", "method": "Data collection", "source": "World Customs Organization. Illicit Trade Report (Brussels: World Customs Organization, 2019)", "sub_topic": "Cultural heritage", "chart_number": "222", "geographical": "Global"}, "description": "The chart presents the shares of selected types of artefacts seized by customs in 2018 and 2019, based on the data from World Customs Organization's \"Illicit trade Report 2019.\" While the number of pieces of currency seized fell from 19 258 to 5 141 pieces (73.3%), coins still represent more than half of all items seized in 2019 (5 141 of 9 399). On the other hand,the number of items seized of the category \"fauna, flora, minerals, anatomy, and fossils\" nearly doubled in 2019 (2 085), compared to 2018 (1 049). "},
{"data": [{"data": [2500000, 688000], "name": "Election fraud mentions"}], "_data": [["Period", "Election fraud mentions"], ["01 January - 08 January 2021", "2500000"], ["09 January - 15 January 2021", "688000"]], "labels": {"name": "Period", "values": ["01 January - 08 January 2021", "09 January - 15 January 2021"]}, "metadata": {"link": "https://www.washingtonpost.com/technology/2021/01/16/misinformation-trump-twitter/", "type": "Solution", "unit": "Number of mentions", "year": "2021", "title": "Twitter Ban Effect on Misinformation About Election Fraud on Social Media", "topic": "Disinformation", "method": "Data collection", "source": "Dwoskin, Elizabeth and Craig Timberg. \"Misinformation Dropped Dramatically the Week after Twitter Banned Trump and Some Allies,\" published in The Washington Post, 16 January 2021", "sub_topic": "Social media accounts", "chart_number": "223", "geographical": "United States"}, "description": "The chart shows how the online misinformation about election fraud changed after several social media sites suspended President Trump and key allies accounts. The new research by Zignal Labs reported that conversations about \nelection fraud dropped from 2.5 million mentions to 688,000 mentions across several social media sites in the week after Trump was banned from Twitter."},
{"data": [{"data": [1874, 3822, 419, 86, 2307, 6917, 775], "name": "Removed under NetzDG (2019)"}, {"data": [8025, 29857, 9043, 21316, 6515, 41342, 10777], "name": "Removed under Google's community guidelines (2019)"}, {"data": [1975, 1514, 125, 61, 230, 1333, 93], "name": "Removed under NetzDG (2020)"}, {"data": [8122, 29380, 25097, 26694, 5969, 49165, 14533], "name": "Removed under Google's community guidelines (2020)"}, {"data": [100, 1347, 50, 103, 109, 243, 23], "name": "Removed under NetzDG (2021)"}, {"data": [1347, 21586, 7586, 14130, 3891, 31366, 10123], "name": "Removed under Google's community guidelines (2021)"}], "_data": [["Reason", "Removed under NetzDG (2019)", "Removed under Google's community guidelines (2019)", "Removed under NetzDG (2020)", "Removed under Google's community guidelines (2020)", "Removed under NetzDG (2021)", "Removed under Google's community guidelines (2021)"], ["Privacy", "1874", "8025", "1975", "8122", "100", "1347"], ["Defamation or insults", "3822", "29857", "1514", "29380", "1347", "21586"], ["Harmful or dangerous acts", "419", "9043", "125", "25097", "50", "7586"], ["Sexual content", "86", "21316", "61", "26694", "103", "14130"], ["Terrorist or unconstitutional content", "2307", "6515", "230", "5969", "109", "3891"], ["Hate speech or political extremism", "6917", "41342", "1333", "49165", "243", "31366"], ["Violence", "775", "10777", "93", "14533", "23", "10123"]], "labels": {"name": "Reason", "values": ["Privacy", "Defamation or insults", "Harmful or dangerous acts", "Sexual content", "Terrorist or unconstitutional content", "Hate speech or political extremism", "Violence"]}, "metadata": {"link": "https://transparencyreport.google.com/netzdg/youtube?community_guidelines_enforcement=period:Y2021H2&lu=community_guidelines_enforcement", "type": "Solution", "unit": "Number of items removed", "year": "2019-2021", "title": "Content Removal Comparison: Google Community Guidelines vs. Germany’s Network Enforcement Act (2019-2021)", "topic": "Illegal Content", "method": "Self-reporting", "source": "Google. Transparency Report: Removals under the Network Enforcement Law (www.google.com, 2022)", "sub_topic": "Removal of illegal content", "chart_number": "224", "geographical": "Germany"}, "description": "The chart presents the distribution of items removed by Google since 2019, due to violations of Google's community guidelines and the Germany’s Network Enforcement Act, on the grounds for removal. The data shows that the majority of removal decisions are based on the platform’s private standards, as they often prioritise the compliance with their community guidelines, and not with German speech laws."},
{"data": [{"data": [88, 108, 158, 464, 504, 303, 1597, 444, 431, 188, 182, 146, 150, 134, 111, 83, 59, 151, 345, 139, 88, 72], "name": "Italy"}, {"data": [133, 133, 162, 205, 250, 176, 294, 204, 177, 131, 93, 97, 152, 239, 59, 27, 39, 227, 323, 114, 69, 69], "name": "Spain"}, {"data": [79, 122, 159, 319, 436, 233, 301, 390, 353, 324, 309, 227, 252, 327, 131, 93, 92, 235, 834, 240, 179, 111], "name": "France"}, {"data": [49, 68, 62, 124, 195, 227, 229, 177, 247, 235, 225, 113, 84, 47, 92, 45, 91, 136, 137, 67, 46, 47], "name": "Germany"}], "_data": [["Period", "Italy", "Spain", "France", "Germany"], ["July 2020", "88", "133", "79", "49"], ["August 2020", "108", "133", "122", "68"], ["September 2020", "158", "162", "159", "62"], ["October 2020", "464", "205", "319", "124"], ["November 2020", "504", "250", "436", "195"], ["December 2020", "303", "176", "233", "227"], ["January 2021", "1597", "294", "301", "229"], ["February 2021", "444", "204", "390", "177"], ["March 2021", "431", "177", "353", "247"], ["April 2021", "188", "131", "324", "235"], ["May 2021", "182", "93", "309", "225"], ["June 2021", "146", "97", "227", "113"], ["July 2021", "150", "152", "252", "84"], ["August 2021", "134", "239", "327", "47"], ["September 2021", "111", "59", "131", "92"], ["October 2021", "83", "27", "93", "45"], ["November 2021", "59", "39", "92", "91"], ["December 2021", "151", "227", "235", "136"], ["January 2022", "345", "323", "834", "137"], ["February 2022", "139", "114", "240", "67"], ["March 2022", "88", "69", "179", "46"], ["April 2022", "72", "69", "111", "47"]], "labels": {"name": "Period", "values": ["July 2020", "August 2020", "September 2020", "October 2020", "November 2020", "December 2020", "January 2021", "February 2021", "March 2021", "April 2021", "May 2021", "June 2021", "July 2021", "August 2021", "September 2021", "October 2021", "November 2021", "December 2021", "January 2022", "February 2022", "March 2022", "April 2022"]}, "metadata": {"link": "https://digital-strategy.ec.europa.eu/en/library/reports-march-and-april-actions-fighting-covid-19-disinformation", "type": "Solution", "unit": "Number of videos", "year": "2020-2022", "title": "Number of Videos Removed Containing the Term “Coronavirus” or “Covid” Found in Violation of TikTok Policy", "topic": "Disinformation", "method": "Self-reporting", "source": "TikTok. March and April 2022 Report EU Code of Practice on Disinformation / COVID-19 (May 2022)", "sub_topic": "Removal of disinformation", "chart_number": "225", "geographical": "France, Germany, Italy, Spain"}, "description": "The chart presents the distribution of videos removed from TikTok, found in violation of the community guidelines, for the period July 2020 to April 2022. The report was produces under the European Commission's Code of Practice on Disinformation monitoring process, and shows the efforts of TikTok to limit the spread of COVID-19 disinformation online. The data covers only four European countries: France, Germany, Italy and Spain."},
{"data": [{"data": [5.93, 4.06, 3.76, 3.65, 3.45, 3.41, 3.18, 2.88, 2.85, 2.82, 2.68, 2.62, 2.49, 2.37, 2.34, 2.34, 2.27, 2.13, 2.1, 1.64, 1.61, 0.99], "name": "Polarisation score"}], "_data": [["Country", "Polarisation score"], ["United States", "5.93"], ["Italy", "4.06"], ["Spain", "3.76"], ["Poland", "3.65"], ["Romania", "3.45"], ["Croatia", "3.41"], ["United Kingdom", "3.18"], ["Hungary", "2.88"], ["France", "2.85"], ["Denmark", "2.82"], ["Greece", "2.68"], ["Austria", "2.62"], ["Slovakia", "2.49"], ["Australia", "2.37"], ["Sweden", "2.34"], ["Czech Republic", "2.34"], ["Norway", "2.27"], ["Finland", "2.13"], ["Netherlands", "2.1"], ["Germany", "1.64"], ["Ireland", "1.61"], ["Portugal", "0.99"]], "labels": {"name": "Country", "values": ["United States", "Italy", "Spain", "Poland", "Romania", "Croatia", "United Kingdom", "Hungary", "France", "Denmark", "Greece", "Austria", "Slovakia", "Australia", "Sweden", "Czech Republic", "Norway", "Finland", "Netherlands", "Germany", "Ireland", "Portugal"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/sites/default/files/Digital%20News%20Report%202017%20web_0.pdf", "type": "Problem", "unit": "Score", "year": "2017", "title": "Online Media Polarisation Score (2017)", "topic": "Disinformation", "method": "Survey (N = 42603)", "source": "Newman, Nic, Richard Fletcher, Antonis Kalogeropoulos, David A. L. Levy and Rasmus Kleis Nielsen. \"Reuters Institute Digital News Report 2017,\" Reuters Institute, 2017", "sub_topic": "Media polarisation", "chart_number": "226", "geographical": "Global"}, "description": "The chart presents the polarisation scores for the countries where the left–right distinction is meaningful, as shown by the Reuters Institute report. The data shows that polarisation amongst the most popular online news brands is highest in the United States, followed by Italy, Spain and Poland. Nordic and Western European countries exhibit lower degrees of polarisation. The results are based on answers to the the survey questions \"Q1F. Some people talk about ‘left’, ‘right’, and ‘centre’ to describe parties and politicians. With this in mind, where would you place yourself on the following scale?\"and \"Q5b. Which of the following brands have you used to access news ONLINE in the last week?\""},
{"data": [{"data": [62, 60, 58, 53, 51, 51, 50, 50, 49, 49, 49, 48, 47, 46, 46, 45, 43, 43, 42, 42, 42, 42, 40, 39, 39, 39, 39, 38, 32, 31, 31, 30, 29, 27, 23, 23], "name": "Share of respondends"}], "_data": [["Country", "Share of respondends"], ["Finland", "62"], ["Brazil", "60"], ["Portugal", "58"], ["Poland", "53"], ["Netherlands", "51"], ["Spain", "51"], ["Germany", "50"], ["Denmark", "50"], ["Canada", "49"], ["Norway", "49"], ["Mexico", "49"], ["Belgium", "48"], ["Chile", "47"], ["Switzerland", "46"], ["Ireland", "46"], ["Austria", "45"], ["United Kingdom", "43"], ["Japan", "43"], ["Sweden", "42"], ["Hong Kong", "42"], ["Australia", "42"], ["Singapore", "42"], ["Turkey", "40"], ["Argentina", "39"], ["Italy", "39"], ["Romania", "39"], ["Croatia", "39"], ["United States", "38"], ["Czech Republic", "32"], ["Hungary", "31"], ["Taiwan", "31"], ["France", "30"], ["Malaysia", "29"], ["Slovakia", "27"], ["Greece", "23"], ["South Korea", "23"]], "labels": {"name": "Country", "values": ["Finland", "Brazil", "Portugal", "Poland", "Netherlands", "Spain", "Germany", "Denmark", "Canada", "Norway", "Mexico", "Belgium", "Chile", "Switzerland", "Ireland", "Austria", "United Kingdom", "Japan", "Sweden", "Hong Kong", "Australia", "Singapore", "Turkey", "Argentina", "Italy", "Romania", "Croatia", "United States", "Czech Republic", "Hungary", "Taiwan", "France", "Malaysia", "Slovakia", "Greece", "South Korea"]}, "metadata": {"link": "https://reutersinstitute.politics.ox.ac.uk/sites/default/files/Digital%20News%20Report%202017%20web_0.pdf", "type": "Problem", "unit": "Share of respondents (%)", "year": "2017", "title": "Overall Trust in News Media (2017)", "topic": "Disinformation", "method": "Survey (N  = 71805)", "source": "Newman, Nic, Richard Fletcher, Antonis Kalogeropoulos, David A. L. Levy and Rasmus Kleis Nielsen. \"Reuters Institute Digital News Report 2017,\" Reuters Institute, 2017", "sub_topic": "Trust in news media", "chart_number": "227", "geographical": "Global"}, "description": "The chart presents the level of trust in the news media across countries, as shown by the Reuters Institute report. The research suggests that the vast majority of news people consume still comes from mainstream media and that most of the reasons for distrust also relate to mainstream media. The results show that highest trust is found in affluent Northern European and Scandinavian countries as well as Portugal and Brazil, while Central, Southern, and Eastern European countries tend to be at the other end of the scale, along with some Asian countries where media are considered to be too close to government. In Greece and South Korea less than a quarter of respondents (23%) agreed that you could trust the news most of the time. The results are based on answers to the the survey question \"Please indicate your level of agreement with the following statements. - I think you can trust most news most of the time/I think I can trust most of the news I consume most of the time\""},
{"data": [{"data": [51, 52, 41, 53, 51, 41, 45, 32, 38, 40, 20], "name": "Less education"}, {"data": [73, 72, 67, 64, 63, 59, 59, 51, 49, 49, 39], "name": "More education"}], "_data": [["Country", "Less education", "More education"], ["Vietnam", "51", "73"], ["Kenya", "52", "72"], ["India", "41", "67"], ["South Africa", "53", "64"], ["Venezuela", "51", "63"], ["Columbia", "41", "59"], ["Philippine", "45", "59"], ["Lebanon", "32", "51"], ["Tunisia", "38", "49"], ["Mexico", "40", "49"], ["Jordan", "20", "39"]], "labels": {"name": "Country", "values": ["Vietnam", "Kenya", "India", "South Africa", "Venezuela", "Columbia", "Philippine", "Lebanon", "Tunisia", "Mexico", "Jordan"]}, "metadata": {"link": "https://www.pewresearch.org/internet/2019/08/22/social-activities-information-seeking-on-subjects-like-health-and-education-top-the-list-of-mobile-activities/", "type": "Problem", "unit": "Share of respondents (%)", "year": "2018", "title": "Mobile Phone Usage on Social Media in Emerging Economies, by Education Levels (2018)", "topic": "Disinformation", "method": "Survey", "source": "Silver, Laura and Christine Huang. \"In Emerging Economies, Smartphone and Social Media Users Have Broader Social Networks,\" published in PEW Research Center, Internet and Technology, 22 August 2019", "sub_topic": "Social media", "chart_number": "228", "geographical": "Global"}, "description": "The chart presents the share of mobile users in Emerging Economies interacting with social media, based on the education level. The results show that mobile phone and social media users with more education are more likely to post on social media social media.The report uses data from to the Mobile Technology and Social Impact 2018 survey, conducted from September to December 2018, by PEW Research Center, on 11 countries. The results are based on respondents' answers to survey question Q20j: \"In the past 12 months, have you used your mobile phone to post your thoughts on social media about an issue that's important to you? (yes/no)\""},
{"data": [{"data": [30, 30, 51, 68, 87, 93, 106, 42, 176, 67, 186, 241, 963, 1262, 919, 579, 927, 735, 962, 350, 222, 76], "name": "Italy"}, {"data": [42, 62, 54, 30, 66, 48, 112, 77, 104, 102, 165, 396, 472, 512, 276, 164, 272, 850, 711, 273, 114, 88], "name": "Spain"}, {"data": [29, 40, 42, 45, 125, 153, 135, 36, 78, 45, 69, 139, 526, 580, 648, 694, 1413, 134, 4891, 918, 407, 145], "name": "France"}, {"data": [47, 90, 54, 126, 123, 318, 204, 145, 348, 354, 290, 216, 1158, 440, 1099, 768, 3332, 3397, 1901, 991, 588, 135], "name": "Germany"}], "_data": [["Period", "Italy", "Spain", "France", "Germany"], ["July 2020", "30", "42", "29", "47"], ["August 2020", "30", "62", "40", "90"], ["September 2020", "51", "54", "42", "54"], ["October 2020", "68", "30", "45", "126"], ["November 2020", "87", "66", "125", "123"], ["December 2020", "93", "48", "153", "318"], ["January 2021", "106", "112", "135", "204"], ["February 2021", "42", "77", "36", "145"], ["March 2021", "176", "104", "78", "348"], ["April 2021", "67", "102", "45", "354"], ["May 2021", "186", "165", "69", "290"], ["June 2021", "241", "396", "139", "216"], ["July 2021", "963", "472", "526", "1158"], ["August 2021", "1262", "512", "580", "440"], ["September 2021", "919", "276", "648", "1099"], ["October 2021", "579", "164", "694", "768"], ["November 2021", "927", "272", "1413", "3332"], ["December 2021", "735", "850", "134", "3397"], ["January 2022", "962", "711", "4891", "1901"], ["February 2022", "350", "273", "918", "991"], ["March 2022", "222", "114", "407", "588"], ["April 2022", "76", "88", "145", "135"]], "labels": {"name": "Period", "values": ["July 2020", "August 2020", "September 2020", "October 2020", "November 2020", "December 2020", "January 2021", "February 2021", "March 2021", "April 2021", "May 2021", "June 2021", "July 2021", "August 2021", "September 2021", "October 2021", "November 2021", "December 2021", "January 2022", "February 2022", "March 2022", "April 2022"]}, "metadata": {"link": "https://digital-strategy.ec.europa.eu/en/library/reports-march-and-april-actions-fighting-covid-19-disinformation", "type": "Solution", "unit": "Number of videos removed", "year": "2020-2022", "title": "Number of Videos Removed Containing Medical Misinformation on TikTok", "topic": "Disinformation", "method": "Self-reporting", "source": "TikTok. March and April 2022 Report EU Code of Practice on Disinformation / COVID-19 (May 2022)", "sub_topic": "Removal of disinformation", "chart_number": "229", "geographical": "France, Germany, Italy, Spain"}, "description": "The chart presents the distribution of videos removed from TikTok, found in violation of the community guidelines, for the period July 2020 to April 2022. The report shows the efforts of TikTok to limit the spread of COVID-19 disinformation online and it is part of the European Commission's Code of Practice on Disinformation monitoring process. The data covers only four European countries: France, Germany, Italy and Spain."},
{"data": [{"data": [1.1, 1.9, 9.4, 3.1, 4.9, 8.2, 5.9, 5.2, 7.6, 6.3, 5.4, 9.6, 8.6, 9, 7.1, 10.6, 7.7, 16.1], "name": "Terrorism"}, {"data": [null, null, null, null, null, null, null, null, 1.6, 4.7, 1.7, 4, 6.4, 9.8, 6.2, 2, 1.6, 2.5], "name": "Organised hate"}], "_data": [["Period", "Terrorism", "Organised hate"], ["October - December 2017", "1.1"], ["January - March 2018", "1.9"], ["April - June 2018", "9.4"], ["July - September 2018", "3.1"], ["October - December 2018", "4.9"], ["January - March 2019", "8.2"], ["April - June 2019", "5.9"], ["July - September 2019", "5.2"], ["October - December 2019", "7.6", "1.6"], ["January - March 2020", "6.3", "4.7"], ["April - June 2020", "5.4", "1.7"], ["July - September 2020", "9.6", "4"], ["October - December 2020", "8.6", "6.4"], ["January - March 2021", "9", "9.8"], ["April - June 2021", "7.1", "6.2"], ["July - September 2021", "10.6", "2"], ["October - December 2021", "7.7", "1.6"], ["January - March 2022", "16.1", "2.5"]], "labels": {"name": "Period", "values": ["October - December 2017", "January - March 2018", "April - June 2018", "July - September 2018", "October - December 2018", "January - March 2019", "April - June 2019", "July - September 2019", "October - December 2019", "January - March 2020", "April - June 2020", "July - September 2020", "October - December 2020", "January - March 2021", "April - June 2021", "July - September 2021", "October - December 2021", "January - March 2022"]}, "metadata": {"link": "https://transparency.fb.com/data/community-standards-enforcement/dangerous-organizations/facebook/", "type": "Solution", "unit": "Pieces of content acted on (million)", "year": "2017-2022", "title": "Content Actioned Under Dangerous Organisations Violations on Facebook", "topic": "Illegal Content", "method": "Self-reporting", "source": "Meta. Transparency Report: Dangerous Organisations: Terrorism and Organized Hate (June 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "230", "geographical": "Global"}, "description": "This chart shows the content actioned under their terrorism and organised hate violations on Facebook, from October 2017 until March 2022. The data shows that the volume of content actioned on under terrorism violations in the first quarter of 2022 almost doubled compared to the same period in the previous year. Content actioned under organised hate violations is more recent (from October 2019). The data shows a slight increase in the first quarter of 2022 compared to the fourth quarter of 2021, but the volume remain significantly lower than the first quarter of 2021."},
{"data": [{"data": [92.4, 94.7, 96.8, 97.7, 95.9, 96.7, 97.9, 98.1], "name": "Content actioned that Instagram found and flagged before users reported it"}, {"data": [7.6, 5.3, 3.2, 2.3, 4.1, 3.3, 2.1, 1.9], "name": "Content actioned that users reported first"}], "_data": [["Period", "Content actioned that Instagram found and flagged before users reported it", "Content actioned that users reported first"], ["April - June 2019", "92.4", "7.6"], ["July - September 2019", "94.7", "5.3"], ["October - December 2019", "96.8", "3.2"], ["January - March 2020", "97.7", "2.3"], ["April - June 2020", "95.9", "4.1"], ["July - September 2020", "96.7", "3.3"], ["October - December 2020", "97.9", "2.1"], ["January - March 2021", "98.1", "1.9"]], "labels": {"name": "Period", "values": ["April - June 2019", "July - September 2019", "October - December 2019", "January - March 2020", "April - June 2020", "July - September 2020", "October - December 2020", "January - March 2021"]}, "metadata": {"link": "https://transparency.fb.com/data/community-standards-enforcement/child-nudity-and-sexual-exploitation/instagram/", "type": "Solution", "unit": "Per cent (%)", "year": "2019-2021", "title": "Percentage of Content Found by Instagram as Containing Child Nudity and Exploitation Compared to the Content Reported by the Users", "topic": "Illegal Content", "method": "Self-reporting", "source": "Facebook. Transparency Report: Percentage of Content Actioned Under Child Nudity and Sexual Exploatation Violations on Intagram (facebook.com, 2021)", "sub_topic": "Prevalence of illegal content", "chart_number": "231", "geographical": "Global"}, "description": "This chart shows the percentage of content found by Instagram as containing child nudity and exploitation compared to the content reported by the users, from April 2019 until March 2021. Since the April 2021, the child nudity and sexual exploitation content was renamed <b>child endangerment</b> and includes two separate categories - <i>nudity and physical abuse</i> and <i>sexual exploitations</i>, which are monitored separately. The data shows that the percentage of the content found by Instagram improved constantly over the years, reaching 98% in the third quarter of 2021."},
{"data": [{"data": [44.1, 42.5, 89, 94.8, 95.1, 93.4, 95.1, 93.8, 91.9, 89.6], "name": "Content actioned that Instagram found and flagged before users reported it"}, {"data": [55.9, 57.5, 11, 5.2, 4.9, 6.6, 4.9, 6.2, 8.1, 10.4], "name": "Content actioned that users reported first"}], "_data": [["Period", "Content actioned that Instagram found and flagged before users reported it", "Content actioned that users reported first"], ["October - December 2019", "44.1", "55.9"], ["January - March 2020", "42.5", "57.5"], ["April - June 2020", "89", "11"], ["July - September 2020", "94.8", "5.2"], ["October - December 2020", "95.1", "4.9"], ["January - March 2021", "93.4", "6.6"], ["April - June 2021", "95.1", "4.9"], ["July - September 2021", "93.8", "6.2"], ["October - December 2021", "91.9", "8.1"], ["January - March 2022", "89.6", "10.4"]], "labels": {"name": "Period", "values": ["October - December 2019", "January - March 2020", "April - June 2020", "July - September 2020", "October - December 2020", "January - March 2021", "April - June 2021", "July - September 2021", "October - December 2021", "January - March 2022"]}, "metadata": {"link": "https://transparency.fb.com/data/community-standards-enforcement/hate-speech/facebook/", "type": "Solution", "unit": "Per cent (%)", "year": "2019-2021", "title": "Percentage of Content Found by Instagram as Hate Speech Compared to the Content Reported by the Users", "topic": "Illegal Content", "method": "Self-reporting", "source": "Meta. Transparency Report: Hate Speech (June 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "232", "geographical": "Global"}, "description": "This chart shows the percentage of content found by Instagram as containing hate speech compared to the content reported by the users, over the period October 2019 - March 2022. Since April 2020, the hate speech detection of Instagram improved significantly compared to the beginning period. However, in the first quarter of 2022, the volume of content found by Instagram declined by 3.8% compared to the same period of the previous year. "},
{"data": [{"data": [0.65, 0.58, 2.5, 6.5, 6.6, 6.3, 9.8, 6, 3.8, 3.4], "name": "Pieces of content actioned on"}], "_data": [["Period", "Pieces of content actioned on"], ["October - December 2019", "0.65"], ["January - March 2020", "0.58"], ["April - June 2020", "2.5"], ["July - September 2020", "6.5"], ["October - December 2020", "6.6"], ["January - March 2021", "6.3"], ["April - June 2021", "9.8"], ["July - September 2021", "6"], ["October - December 2021", "3.8"], ["January - March 2022", "3.4"]], "labels": {"name": "Period", "values": ["October - December 2019", "January - March 2020", "April - June 2020", "July - September 2020", "October - December 2020", "January - March 2021", "April - June 2021", "July - September 2021", "October - December 2021", "January - March 2022"]}, "metadata": {"link": "https://transparency.facebook.com/community-standards-enforcement#instagram-hate-speech", "type": "Solution", "unit": "Pieces of content actioned on (million)", "year": "2019-2022", "title": "Content Actioned Under Hate Speech Violations on Instagram ", "topic": "Illegal Content", "method": "Self-reporting", "source": "Meta. Transparency Report: Hate Speech (June 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "233", "geographical": "Global"}, "description": "The chart shows the number pieces of content actioned under hate speech violations on Instagram over the period October 2019 - March 2022. The data shows a significant increase of hate speech violations that were found and actioned on by Instagram during the monitoring period, with a pick in the second quarter of 2021, when 9.8 million of pieces of content were actioned on. In the first quarter of 2022, the volume of content actioned on is significantly lower (3.4 million of pieces of content)."},
{"data": [{"data": [null, null, 45.3, 47, 49.4, 43.3, 32.7, 31.4, 24.7, 23.1, 25.8, 25.5], "name": "Adult Nudity & Sexual Activity"}, {"data": [null, null, 10.8, 8.7, 10.5, 8.6, 14.2, 16.9, 11.8, 16.5, 15.1, 17.2], "name": "Bullying & Harassment"}, {"data": [17.5, 22.9, 4.9, 5.8, 1, 3.3, 2.3, 2.5, 4.9, 4.5, 8.2, 5.2], "name": "Child Endagerment"}, {"data": [null, null, 1, 1, 1, 0.7, 0.9, 1, 1, 0.6, 0.8, 1.2], "name": "Organized Hate"}, {"data": [3.6, 4.1, 0.6, 2.6, 0.9, 0.6, 1, 1.3, 0.9, 1.4, 2.1, 3.7], "name": "Terrorism"}, {"data": [null, null, 4.6, 3.3, 16.5, 21.5, 18.8, 19, 25.8, 12.7, 8.7, 8.3], "name": "Hate Speech"}, {"data": [51.1, 47.3, 9.7, 7.9, 5.4, 4.1, 4.2, 3.3, 3.1, 4.1, 3.2, 4.8], "name": "Regulated Goods"}, {"data": [27.9, 25.7, 6.5, 7.5, 1.3, 4.3, 9.7, 8.1, 7.9, 7.4, 17.8, 12.5], "name": "Suicide and Self-Injury"}, {"data": [null, null, 16.5, 16.2, 13.8, 13.6, 16.2, 16.6, 20, 22.6, 12.6, 15], "name": "Violent & Graphic Content"}, {"data": [null, null, null, null, null, null, null, null, null, 7, 5.9, 6.6], "name": "Violence and Incitement"}], "_data": [["Period", "Adult Nudity & Sexual Activity", "Bullying & Harassment", "Child Endagerment", "Organized Hate", "Terrorism", "Hate Speech", "Regulated Goods", "Suicide and Self-Injury", "Violent & Graphic Content", "Violence and Incitement"], ["April - June 2019", "", "", "17.5", "", "3.6", "", "51.1", "27.9"], ["July - September 2019", "", "", "22.9", "", "4.1", "", "47.3", "25.7"], ["October - December 2019", "45.3", "10.8", "4.9", "1.0", "0.6", "4.6", "9.7", "6.5", "16.5"], ["January - March 2020", "47.0", "8.7", "5.8", "1.0", "2.6", "3.3", "7.9", "7.5", "16.2"], ["April - June 2020", "49.4", "10.5", "1.0", "1.0", "0.9", "16.5", "5.4", "1.3", "13.8"], ["July - September 2020", "43.3", "8.6", "3.3", "0.7", "0.6", "21.5", "4.1", "4.3", "13.6"], ["October - December 2020", "32.7", "14.2", "2.3", "0.9", "1.0", "18.8", "4.2", "9.7", "16.2"], ["January - March 2021", "31.4", "16.9", "2.5", "1.0", "1.3", "19.0", "3.3", "8.1", "16.6"], ["April - June 2021", "24.7", "11.8", "4.9", "1.0", "0.9", "25.8", "3.1", "7.9", "20.0"], ["July - September 2021", "23.1", "16.5", "4.5", "0.6", "1.4", "12.7", "4.1", "7.4", "22.6", "7.0"], ["October - December 2021", "25.8", "15.1", "8.2", "0.8", "2.1", "8.7", "3.2", "17.8", "12.6", "5.9"], ["January - March 2022", "25.5", "17.2", "5.2", "1.2", "3.7", "8.3", "4.8", "12.5", "15.0", "6.6"]], "labels": {"name": "Period", "values": ["April - June 2019", "July - September 2019", "October - December 2019", "January - March 2020", "April - June 2020", "July - September 2020", "October - December 2020", "January - March 2021", "April - June 2021", "July - September 2021", "October - December 2021", "January - March 2022"]}, "metadata": {"link": "https://transparency.fb.com/data/community-standards-enforcement/", "type": "Solution", "unit": "Per cent (%)", "year": "2019-2022", "title": "Distribution of the Content Actioned on Instagram, by Reason of Removal", "topic": "Illegal Content", "method": "Self-reporting", "source": "Meta. Transparency Report: Community Standards Enforcement Report (June 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "234", "geographical": "Global"}, "description": "The chart shows the distribution of the content actioned on Instagram, by reasons of removal, from the fourth quarter of 2019 until the first quarter of 2022. A metric for a new policy area called <b>violence and incitement</b> was added to the Community Standards in the third quarter of 2021. Additionally, starting with the second quarter of 2021, the child nudity and sexual abuse category was renamed <b>child endagerment</b> and collects data on two separate topics: sexual exploitation and nudity and physical abuse. The data shows that adult nudity and sexual activity remain the main reason of removal of content, followed by bullying and harassment content and violent and graphic one.  "},
{"data": [{"data": [48, 40.27, 44.17, 37.35, 36.73, 53.93, 39.64, 46.05, 26.7, 45.86, 47.02, 39.22, 54.04, 55.25, 63.95, 65.9, 56.05, 45.05], "name": "Fake accounts"}, {"data": [50.29, 57.74, 52.81, 59.44, 61.22, 44.12, 58.14, 51.47, 70.38, 51.25, 49.62, 57.32, 41.57, 39.05, 29.88, 28.45, 39.56, 50.68], "name": "Spam"}, {"data": [1.71, 1.99, 3.02, 3.21, 2.05, 1.95, 2.21, 2.47, 2.92, 2.89, 3.36, 3.45, 4.39, 5.7, 6.17, 5.65, 4.39, 4.28], "name": "Other types of violations"}], "_data": [["Period", "Fake accounts", "Spam", "Other types of violations"], ["October - December 2017", "48", "50.29", "1.71"], ["January - March 2018", "40.27", "57.74", "1.99"], ["April - June 2018", "44.17", "52.81", "3.02"], ["July - September 2018", "37.35", "59.44", "3.21"], ["October - December 2018", "36.73", "61.22", "2.05"], ["January - March 2019", "53.93", "44.12", "1.95"], ["April - June 2019", "39.64", "58.14", "2.21"], ["July - September 2019", "46.05", "51.47", "2.47"], ["October - December 2019", "26.7", "70.38", "2.92"], ["January - March 2020", "45.86", "51.25", "2.89"], ["April - June 2020", "47.02", "49.62", "3.36"], ["July - September 2020", "39.22", "57.32", "3.45"], ["October - December 2020", "54.04", "41.57", "4.39"], ["January - March 2021", "55.25", "39.05", "5.7"], ["April - June 2021", "63.95", "29.88", "6.17"], ["July - September 2021", "65.9", "28.45", "5.65"], ["October - December 2021", "56.05", "39.56", "4.39"], ["January - March 2022", "45.05", "50.68", "4.28"]], "labels": {"name": "Period", "values": ["October - December 2017", "January - March 2018", "April - June 2018", "July - September 2018", "October - December 2018", "January - March 2019", "April - June 2019", "July - September 2019", "October - December 2019", "January - March 2020", "April - June 2020", "July - September 2020", "October - December 2020", "January - March 2021", "April - June 2021", "July - September 2021", "October - December 2021", "January - March 2022"]}, "metadata": {"link": "https://transparency.fb.com/data/community-standards-enforcement/", "type": "Solution", "unit": "Per cent (%)", "year": "2019-2022", "title": "Share of Fake Accounts and Spam Content Actioned on Facebook (2017-2022)", "topic": "Illegal Content", "method": "Self-reporting", "source": "Meta. Transparency Report: Community Standards Enforcement Report (June 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "235", "geographical": "Global"}, "description": "The chart shows the share of fake accounts and spam content actioned on Facebook, from the fourh quarter of 2017 until the first quarter of 2022. While these two violations remain the main reasons of removal of content on Facebook, the data shows that the other types of violations (such as adult nudity and sexual activity, child nudity and sexual exploitation, bullying and harassment, dangerous organisations, hate speech, and violent and graphic content) have also increased during this period."},
{"data": [{"data": [84.2, 72.9, 63.8, 49.1, 42.8, 30.8, 33.2, 33.2, 32.3, 36.9, 35.6, 32.1, 26.6, 23.8, 20, 22.5, 20.5, 20.4], "name": "Adult Nudity & Sexual Activity"}, {"data": [null, null, null, 3.1, 4, 3, 3, 3.5, 2.3, 2.1, 2.5, 3.1, 6, 6.6, 4.8, 6, 6.2, 6.3], "name": "Bullying & Harassment"}, {"data": [null, null, null, 13.9, 10.7, 7.3, 8.2, 12.5, 11, 7.9, 5, 10.8, 5, 3.7, 17, 14.9, 16.2, 12.2], "name": "Child Endagerment"}, {"data": [null, null, null, null, null, null, null, null, 1.3, 4.4, 3, 3.5, 6.1, 7.3, 3.8, 1.3, 1.2, 1.6], "name": "Organized Hate"}, {"data": [4.5, 6.6, 17.2, 4.8, 7.3, 10.3, 7, 5.7, 6.3, 5.9, 9.6, 8.4, 8.1, 6.7, 4.3, 6.9, 5.8, 10.6], "name": "Terrorism"}, {"data": [6.5, 8.7, 4.6, 4.5, 5.1, 5, 5.1, 7.6, 4.6, 8.9, 24.4, 19.3, 25.5, 18.8, 19.2, 14.5, 13.1, 9.9], "name": "Hate Speech"}, {"data": [null, null, null, null, 1.9, 1.8, 6.1, 7.3, 9.1, 8.7, 4.2, 5.1, 5.3, 3.8, 2.3, 2.5, 4.1, 3], "name": "Regulated Goods"}, {"data": [null, null, null, null, null, null, 2.4, 2.7, 4.2, 1.7, 1.1, 1.2, 2.5, 3.9, 10.2, 5.5, 4.6, 4.5], "name": "Suicide and Self-Injury"}, {"data": [4.9, 11.8, 14.4, 24.6, 28.2, 41.8, 34.9, 27.5, 28.8, 23.5, 14.7, 16.6, 15, 25.4, 18.3, 17.2, 18.9, 17.2], "name": "Violent & Graphic Content"}, {"data": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 8.8, 9.3, 14.3], "name": "Violence and Incitement"}], "_data": [["Period", "Adult Nudity & Sexual Activity", "Bullying & Harassment", "Child Endagerment", "Organized Hate", "Terrorism", "Hate Speech", "Regulated Goods", "Suicide and Self-Injury", "Violent & Graphic Content", "Violence and Incitement"], ["October - December 2017", "84.2", "", "", "", "4.5", "6.5", "", "", "4.9"], ["January - March 2018", "72.9", "", "", "", "6.6", "8.7", "", "", "11.8"], ["April - June 2018", "63.8", "", "", "", "17.2", "4.6", "", "", "14.4"], ["July - September 2018", "49.1", "3.1", "13.9", "", "4.8", "4.5", "", "", "24.6"], ["October - December 2018", "42.8", "4.0", "10.7", "", "7.3", "5.1", "1.9", "", "28.2"], ["January - March 2019", "30.8", "3.0", "7.3", "", "10.3", "5.0", "1.8", "", "41.8"], ["April - June 2019", "33.2", "3.0", "8.2", "", "7.0", "5.1", "6.1", "2.4", "34.9"], ["July - September 2019", "33.2", "3.5", "12.5", "", "5.7", "7.6", "7.3", "2.7", "27.5"], ["October - December 2019", "32.3", "2.3", "11.0", "1.3", "6.3", "4.6", "9.1", "4.2", "28.8"], ["January - March 2020", "36.9", "2.1", "7.9", "4.4", "5.9", "8.9", "8.7", "1.7", "23.5"], ["April - June 2020", "35.6", "2.5", "5.0", "3.0", "9.6", "24.4", "4.2", "1.1", "14.7"], ["July - September 2020", "32.1", "3.1", "10.8", "3.5", "8.4", "19.3", "5.1", "1.2", "16.6"], ["October - December 2020", "26.6", "6.0", "5.0", "6.1", "8.1", "25.5", "5.3", "2.5", "15.0"], ["January - March 2021", "23.8", "6.6", "3.7", "7.3", "6.7", "18.8", "3.8", "3.9", "25.4"], ["April - June 2021", "20.0", "4.8", "17.0", "3.8", "4.3", "19.2", "2.3", "10.2", "18.3"], ["July - September 2021", "22.5", "6.0", "14.9", "1.3", "6.9", "14.5", "2.5", "5.5", "17.2", "8.8"], ["October - December 2021", "20.5", "6.2", "16.2", "1.2", "5.8", "13.1", "4.1", "4.6", "18.9", "9.3"], ["January - March 2022", "20.4", "6.3", "12.2", "1.6", "10.6", "9.9", "3.0", "4.5", "17.2", "14.3"]], "labels": {"name": "Period", "values": ["October - December 2017", "January - March 2018", "April - June 2018", "July - September 2018", "October - December 2018", "January - March 2019", "April - June 2019", "July - September 2019", "October - December 2019", "January - March 2020", "April - June 2020", "July - September 2020", "October - December 2020", "January - March 2021", "April - June 2021", "July - September 2021", "October - December 2021", "January - March 2022"]}, "metadata": {"link": "https://transparency.fb.com/data/community-standards-enforcement/", "type": "Solution", "unit": "Per cent (%)", "year": "2019-2022", "title": "Distribution of Content Actioned Under Other Types of Violation on Facebook (2017-2022)", "topic": "Illegal Content", "method": "Self-reporting", "source": "Meta. Transparency Report: Community Standards Enforcement Report (June 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "236", "geographical": "Global"}, "description": "The chart shows the distribution of the content actioned under other types of violations on Facebook, from the fourh quarter of 2017 until the first quarter of 2022. A metric for a new policy area called <b>violence and incitement</b> was added to the Community Standards in the third quarter of 2021. Additionally, starting with the second quarter of 2021, the child nudity and sexual abuse category was renamed <b>child endagerment</b> and collects data on two separate topics: sexual exploitation and nudity and physical abuse. The data shows that the adult nudity and sexual activity remains the main reason of removal on Facebook, followed by violent and graphic content, violence and incitement and child endagerment. The chart excludes the content removed under fake accounts and spam content violations."},
{"data": [{"data": [183075, 1165481, 2221833, 1441015, 499940, 323573, 1792377, 258694, 4083493, 1527926, 350454, 1583881, 5735330, 1345635, 1905877, 821867, 9022800], "name": "European market"}, {"data": [644103, 2705944, 4637177, 3391473, 1304403, 994053, 3599537, 1445010, 17369163, 7056353, 1347499, 2878058, 4960950, 2818793, 3390012, 1421265, 6155244], "name": "Other markets"}], "_data": [["Period", "European market", "Other markets"], ["July 2020", "183075", "644103"], ["August 2020", "1165481", "2705944"], ["September 2020", "2221833", "4637177"], ["October 2020", "1441015", "3391473"], ["November 2020", "499940", "1304403"], ["December 2020", "323573", "994053"], ["January 2021", "1792377", "3599537"], ["February 2021", "258694", "1445010"], ["March 2021", "4083493", "17369163"], ["April 2021", "1527926", "7056353"], ["May 2021", "350454", "1347499"], ["June 2021", "1583881", "2878058"], ["July - August 2021", "5735330", "4960950"], ["September - October 2021", "1345635", "2818793"], ["November - December 2021", "1905877", "3390012"], ["January - February 2022", "821867", "1421265"], ["March - April 2022", "9022800", "6155244"]], "labels": {"name": "Period", "values": ["July 2020", "August 2020", "September 2020", "October 2020", "November 2020", "December 2020", "January 2021", "February 2021", "March 2021", "April 2021", "May 2021", "June 2021", "July - August 2021", "September - October 2021", "November - December 2021", "January - February 2022", "March - April 2022"]}, "metadata": {"link": "https://digital-strategy.ec.europa.eu/en/library/reports-march-and-april-actions-fighting-covid-19-disinformation", "type": "Solution", "unit": "Number of submissions blocked", "year": "2020-2022", "title": "Number Advertiser Submissions Blocked by Microsoft for Containing Disinformation About COVID-19", "topic": "Disinformation", "method": "Self-reporting", "source": "Microsoft. Efforts to Tackle COVID-19 Disinformation (May 2022)", "sub_topic": "Removal of disinformation", "chart_number": "237", "geographical": "Global"}, "description": "The chart shows the number of advertisments submissions blocked by Microsoft under their Misleading Content policy for combating COVID-19 disinformation. The data used are those reported by Microsft under the European Union Code of Practice on Disinformation and covers the period July 2020 - April 2022. Since October 2020, the data reported includes also data on vaccine-related disinformation."},
{"data": [{"data": [12630615, 9258193, 4593519, 2774701, 846423, 725743, 577313, 530876, 490889, 414524, 226084, 195659, 95075, 87170, 65089, 59900, 59878, 43209, 39980, 39946, 38743, 20531, 17538, 15012, 13589, 13183, 9297], "name": "Blocked Ads"}], "_data": [["Country", "Blocked Ads"], ["Germany", "12630615.0"], ["Austria", "9258193.0"], ["Netherlands", "4593519.0"], ["Spain", "2774701.0"], ["Poland", "846423.0"], ["France", "725743"], ["Ireland", "577313"], ["Belgium", "530876"], ["Czechia", "490889"], ["Italy", "414524"], ["Denmark", "226084"], ["Sweden", "195659"], ["Latvia", "95075.0"], ["Greece", "87170.0"], ["Romania", "65089.0"], ["Portugal", "59900.0"], ["Slovakia", "59878.0"], ["Cyprus", "43209.0"], ["Finland", "39980"], ["Hungary", "39946"], ["Bulgaria", "38743"], ["Croatia", "20531"], ["Lithuania", "17538"], ["Malta", "15012"], ["Luxembourg", "13589"], ["Estonia", "13183"], ["Slovenia", "9297"]], "labels": {"name": "Country", "values": ["Germany", "Austria", "Netherlands", "Spain", "Poland", "France", "Ireland", "Belgium", "Czechia", "Italy", "Denmark", "Sweden", "Latvia", "Greece", "Romania", "Portugal", "Slovakia", "Cyprus", "Finland", "Hungary", "Bulgaria", "Croatia", "Lithuania", "Malta", "Luxembourg", "Estonia", "Slovenia"]}, "metadata": {"link": "https://digital-strategy.ec.europa.eu/en/library/reports-march-and-april-actions-fighting-covid-19-disinformation", "type": "Solution", "unit": "Number of ads blocked", "year": "2020-2022", "title": "Number of Coronavirus-Related Ads Blocked or Removed by Google for Policy Violations", "topic": "Disinformation", "method": "Self-reporting", "source": "Google. EU and COVID-19 Disinformation Google Report (May 2022)", "sub_topic": "Removal of disinformation", "chart_number": "238", "geographical": "European Union"}, "description": "The chart shows the number of Coronavirus-related ads removed or blocked by Google for policy violations, since January 2020 until April 2022. The data used are those reported by Google under the European Union Code of Practice on Disinformation monitoring programme."},
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{"data": [{"data": [1226872, 1914471, 2863181, 1927063, 4470600, 5913337], "name": "Content removed"}], "_data": [["Topic", "Content removed"], ["July - December 2018", "1226872.0"], ["January - June 2019", "1914471.0"], ["July - December 2019", "2863181.0"], ["January - June 2020", "1927063.0"], ["July - December 2020", "4470600.0"], ["January - June 2021", "5913337.0"]], "labels": {"name": "Topic", "values": ["July - December 2018", "January - June 2019", "July - December 2019", "January - June 2020", "July - December 2020", "January - June 2021"]}, "metadata": {"link": "https://transparency.twitter.com/en/reports/rules-enforcement.html#2021-jan-jun", "type": "Solution", "unit": "Pieces of content removed", "year": "2018-2021", "title": "Content Removed by Twitter for Policy Violations", "topic": "Illegal Content", "method": "Self-reporting", "source": "Twitter. Twitter Transparency Report (transparency.twitter.com, 2022)", "sub_topic": "Removal of illegal content", "chart_number": "242", "geographical": "Global"}, "description": "The chart shows the amount of content removed by Twitter due to Twitter policy violations. The data shows 32% increase of the amount of content removed in the period January-June 2021 compared to the previous period, and it is three times higher compared to the same period of the previous year."},
{"data": [{"data": [775909, 687397, 872855, 925744, 1009083, 1240148], "name": "Accounts suspended"}], "_data": [["Topic", "Accounts suspended"], ["July - December 2018", "775909.0"], ["January - June 2019", "687397.0"], ["July - December 2019", "872855.0"], ["January - June 2020", "925744.0"], ["July - December 2020", "1009083.0"], ["January - June 2021", "1240148.0"]], "labels": {"name": "Topic", "values": ["July - December 2018", "January - June 2019", "July - December 2019", "January - June 2020", "July - December 2020", "January - June 2021"]}, "metadata": {"link": "https://transparency.twitter.com/en/reports/rules-enforcement.html#2021-jan-jun", "type": "Solution", "unit": "Pieces of content removed", "year": "2018-2021", "title": "Number of Accounts Suspended by Twitter for Policy Violations", "topic": "Illegal Content", "method": "Self-reporting", "source": "Twitter. Twitter Transparency Report (transparency.twitter.com, 2022)", "sub_topic": "Removal of illegal content", "chart_number": "243", "geographical": "Global"}, "description": "The chart shows the number of accounts suspended by Twitter due to Twitter policy violations. The data shows 23% increase of the number of accounts suspended in the period January-June 2021 compared to the previous period, and 34% increase compared to the same period of the preivous year."},
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{"data": [{"data": [2568474, 2946164, 2910061, 3093439, 3036689, 2935368, 3050180, 3268131, 3216952, 3259730, 3312346], "name": "Germany"}, {"data": [1321431, 1249757, 1230276, 1208976, 1193137, 1163863, 1146465, 1203574, 1293992, 1313993, 1316310], "name": "Spain"}, {"data": [1517979, 1613464, 1656546, 1635498, 1636776, 1630156, 1630790, 1540758, 1649468, 1705237, 1673036], "name": "France"}, {"data": [1010238, 987155, 1024163, 1029116, 1038369, 1027347, 1023369, 1036287, 1085984, 1130449, 1146404], "name": "Italy"}, {"data": [695708, 693234, 701554, 712983, 708757, 709505, 712905, 724881, 727100, 748134, 768452], "name": "Netherlands"}, {"data": [922951, 885222, 879252, 876596, 859425, 849779, 879483, 897250, 933251, 922945, 1007092], "name": "Poland"}, {"data": [256608, 253388, 260168, 264169, 265422, 268223, 270471, 272746, 281088, 282158, 285088], "name": "Sweden"}, {"data": [3002957, 2962686, 2866627, 2911053, 2931934, 2951481, 3042370, 3072975, 2808045, 3067719, 3108695], "name": "United Kingdom"}], "_data": [["Year", "Germany", "Spain", "France", "Italy", "Netherlands", "Poland", "Sweden", "United Kingdom"], ["2008", "2568474", "1321431", "1517979", "1010238", "695708", "922951", "256608", "3002957"], ["2009", "2946164", "1249757", "1613464", "987155", "693234", "885222", "253388", "2962686"], ["2010", "2910061", "1230276", "1656546", "1024163", "701554", "879252", "260168", "2866627"], ["2011", "3093439", "1208976", "1635498", "1029116", "712983", "876596", "264169", "2911053"], ["2012", "3036689", "1193137", "1636776", "1038369", "708757", "859425", "265422", "2931934"], ["2013", "2935368", "1163863", "1630156", "1027347", "709505", "849779", "268223", "2951481"], ["2014", "3050180", "1146465", "1630790", "1023369", "712905", "879483", "270471", "3042370"], ["2015", "3268131", "1203574", "1540758", "1036287", "724881", "897250", "272746", "3072975"], ["2016", "3216952", "1293992", "1649468", "1085984", "727100", "933251", "281088", "2808045"], ["2017", "3259730", "1313993", "1705237", "1130449", "748134", "922945", "282158", "3067719"], ["2018", "3312346", "1316310", "1673036", "1146404", "768452", "1007092", "285088", "3108695"]], "labels": {"name": "Year", "values": ["2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018"]}, "metadata": {"link": "https://ec.europa.eu/eurostat/databrowser/view/SBS_NA_DT_R2__custom_1146394/default/table?lang=en", "type": "Problem", "unit": "Values", "year": "2008-2018", "title": "The Evolution of the Number of Retail Jobs (2008-2018)", "topic": "Illegal Products", "method": "Data collection", "source": "Eurostat. Annual Detailed Enterprise Statistics for Trade (NACE Rev. 2 G), last accessed 14 July 2021", "sub_topic": "e-Commerce", "chart_number": "248", "geographical": "Europe"}, "description": "The chart presents the evolution of the e-commerce jobs since 2008, in eigth European countries. Data refers to the number of employees in the trade sector G47: Retail trade, except of motor vehicles and motorcycles, excluding the subsector G478: Retail sale via stalls and markets."},
{"data": [{"data": [144000, 80000, 31000, 24000, 20000, 9000, 8000, 6000], "name": "E-commerce jobs"}], "_data": [["Year", "E-commerce jobs"], ["Germany", "144000"], ["United Kingdom", "80000"], ["France", "31000"], ["Poland", "24000"], ["Netherlands", "20000"], ["Sweden", "9000"], ["Italy", "8000"], ["Spain", "6000"]], "labels": {"name": "Year", "values": ["Germany", "United Kingdom", "France", "Poland", "Netherlands", "Sweden", "Italy", "Spain"]}, "metadata": {"link": "https://www.oliverwyman.com/content/dam/oliver-wyman/v2/publications/2021/apr/is-ecommerce-good-for-europe.pdf", "type": "Problem", "unit": "Values", "year": "2008-2018", "title": "The Number of E-commerce Jobs Created (2008-2018)", "topic": "Illegal Products", "method": "Data collection", "source": "\"Is E-commerce Good for Europe?\" published in Oliver Wyman Insights, 2021", "sub_topic": "e-Commerce", "chart_number": "249", "geographical": "Europe"}, "description": "The chart presents the number of e-commerce jobs created over the period 2008-2018, in eigth European countries. Data are based on the results of the economic and environmental impact study \"Is E-commerce Good for Europe?\" led Oliver Wyman in 2020. Between 2008 and 2018, 1.3 million net direct retail jobs were created in the eight countries, out of which over 300,000 jobs were created by e-commerce retailers."},
{"data": [{"data": [547, 1051, 1096, 953, 969, null, 452, 617], "name": "Online"}, {"data": [2959, 7526, 4197, 4036, 4517, null, 2208, 4678], "name": "Physical retail by car"}], "_data": [["Country", "Online", "Physical retail by car"], ["France (National)", "547", "2959"], ["Germany (National)", "1051", "7526"], ["Italy (National)", "1096", "4197"], ["Spain (National)", "953", "4036"], ["United Kingdom (National)", "969", "4517"], [" "], ["France (Paris)", "452", "2208"], ["United Kingdom (London)", "617", "4678"]], "labels": {"name": "Country", "values": ["France (National)", "Germany (National)", "Italy (National)", "Spain (National)", "United Kingdom (National)", " ", "France (Paris)", "United Kingdom (London)"]}, "metadata": {"link": "https://www.oliverwyman.com/content/dam/oliver-wyman/v2/publications/2021/apr/is-ecommerce-good-for-europe.pdf", "type": "Problem", "unit": "grams of CO2e", "year": "2008-2018", "title": "The Impact of CO2e (in grams) for Physical Retail and E-commerce for Fashion Retail", "topic": "Illegal Products", "method": "Data collection", "source": "\"Is E-commerce Good for Europe?\" published in Oliver Wyman Insights, 2021", "sub_topic": "e-Commerce", "chart_number": "250", "geographical": "Europe"}, "description": "The chart presents the impact of CO2 equivalent (in grams) between e-commerce and in-store shopping for fashion retail, in eigth European countries. Data are based on the results of the economic and environmental impact study \"Is E-commerce Good for Europe?\" led Oliver Wyman in 2020. The results show that, on average, the CO2e impact of in-store shopping is five times higher than the online option.  "},
{"data": [{"data": [394, 898, 943, 801, 816, null, 299, 464], "name": "Online"}, {"data": [2219, 2355, 2545, 3353, 2800, null, 1888, 2309], "name": "Physical retail by car"}], "_data": [["Country", "Online", "Physical retail by car"], ["France (National)", "394", "2219"], ["Germany (National)", "898", "2355"], ["Italy (National)", "943", "2545"], ["Spain (National)", "801", "3353"], ["United Kingdom (National)", "816", "2800"], [" "], ["France (Paris)", "299", "1888"], ["United Kingdom (London)", "464", "2309"]], "labels": {"name": "Country", "values": ["France (National)", "Germany (National)", "Italy (National)", "Spain (National)", "United Kingdom (National)", " ", "France (Paris)", "United Kingdom (London)"]}, "metadata": {"link": "https://www.oliverwyman.com/content/dam/oliver-wyman/v2/publications/2021/apr/is-ecommerce-good-for-europe.pdf", "type": "Problem", "unit": "grams of CO2e", "year": "2008-2018", "title": "The Impact of CO2e (in grams) for Physical Retail and E-commerce for Books", "topic": "Illegal Products", "method": "Data collection", "source": "\"Is E-commerce Good for Europe?\" published in Oliver Wyman Insights, 2021", "sub_topic": "e-Commerce", "chart_number": "251", "geographical": "Europe"}, "description": "The chart presents the impact of CO2 equivalent (in grams) between e-commerce and in-store shopping for books retail, in eigth European countries. Data are based on the results of the economic and environmental impact study \"Is E-commerce Good for Europe?\" led Oliver Wyman in 2020. The results show that, on average, the CO2e impact of in-store shopping is 3.7 times higher than the online option.  "},
{"data": [{"data": [432, 936, 981, 839, 854, null, 337, 502], "name": "Online"}, {"data": [2283, 2775, 2834, 3225, 2490, null, 2529, 2383], "name": "Physical retail by car"}], "_data": [["Country", "Online", "Physical retail by car"], ["France (National)", "432", "2283"], ["Germany (National)", "936", "2775"], ["Italy (National)", "981", "2834"], ["Spain (National)", "839", "3225"], ["United Kingdom (National)", "854", "2490"], [" "], ["France (Paris)", "337", "2529"], ["United Kingdom (London)", "502", "2383"]], "labels": {"name": "Country", "values": ["France (National)", "Germany (National)", "Italy (National)", "Spain (National)", "United Kingdom (National)", " ", "France (Paris)", "United Kingdom (London)"]}, "metadata": {"link": "https://www.oliverwyman.com/content/dam/oliver-wyman/v2/publications/2021/apr/is-ecommerce-good-for-europe.pdf", "type": "Problem", "unit": "grams of CO2e", "year": "2008-2018", "title": "The Impact of CO2e (in grams) for Physical Retail and E-commerce for Consumer Electronics", "topic": "Illegal Products", "method": "Data collection", "source": "\"Is E-commerce Good for Europe?\" published in Oliver Wyman Insights, 2021", "sub_topic": "e-Commerce", "chart_number": "252", "geographical": "Europe"}, "description": "The chart presents the impact of CO2 equivalent (in grams) between e-commerce and in-store shopping for consumer electronics retail, in eigth European countries. Data are based on the results of the economic and environmental impact study \"Is E-commerce Good for Europe?\" led Oliver Wyman in 2020. The results show that, on average at national level, the CO2e impact of in-store shopping is 3.5 times higher than the online option.   "},
{"data": [{"data": [6673.7], "name": "Health care cost due to non-fatal injuries"}, {"data": [1802.7], "name": "Productivity loss due to non-fatal injuries"}, {"data": [28396.7], "name": "The loss of quality of life, in hospitalized cases, due to non-fatal injuries"}, {"data": [39792.8], "name": "Premature death due to fatal injuries"}], "_data": [["category", "Health care cost due to non-fatal injuries", "Productivity loss due to non-fatal injuries", "The loss of quality of life, in hospitalized cases, due to non-fatal injuries", "Premature death due to fatal injuries"], ["Costs", "6673.7", "1802.7", "28396.7", "39792.8"]], "labels": {"name": "category", "values": ["Costs"]}, "metadata": {"link": "https://ec.europa.eu/info/sites/default/files/study-part2-ia_1_1.pdf", "type": "Problem", "unit": "million Euro", "year": "2017", "title": "The Detriment Suffered by European Union Consumers and Society per Year Due to Product-Related Injuries (2017)", "topic": "Illegal Products", "method": "Administrative data", "source": "European Commission. Study to Support the Preparation of an Evaluation of the General Product Safety Directive As Well As of an Impact Assessment on Its Potential Revision - Part 2: Impact Assessment (Brussels: European Commission, June 2021)", "sub_topic": "Product safety", "chart_number": "253", "geographical": "European Union"}, "description": "The chart presents the estimated prejudice caused by all product-related injuries to consumers in the European Union in 2017, based on the results of the impact assessment study realised for the evaluation of the General Product Safety Directive and its potential revision for the European Commission. The results show an estimate of 76.6 billion euro per year in financial prejudice to EU consumers and society due to product-related injuries. Out of the total amount, 48% of this prejudice is related to non-fatal injuries. These estimates exclude losses caused by work and transportation accidents."},
{"data": [{"data": [4, 9], "name": "Unsafe products"}, {"data": [96, 91], "name": "Safe products"}], "_data": [["Type of Respondents", "Unsafe products", "Safe products"], ["Brick-and-mortar shops", "4", "96"], ["Online", "9", "91"]], "labels": {"name": "Type of Respondents", "values": ["Brick-and-mortar shops", "Online"]}, "metadata": {"link": "https://ec.europa.eu/info/sites/default/files/study-part2-ia_1_1.pdf", "type": "Problem", "unit": "Percent (%)", "year": "2020", "Range": "0 100", "title": "The Estimated Share of Unsafe Products on the Market (2020)", "topic": "Illegal Products", "method": "Survey (N=153)", "source": "European Commission. Study to Support the Preparation of an Evaluation of the General Product Safety Directive As Well As of an Impact Assessment on Its Potential Revision - Part 2: Impact Assessment (Brussels: European Commission, June 2021)", "sub_topic": "Product safety", "chart_number": "254", "geographical": "European Union"}, "description": "The chart presents the estimates of the share of unsafe products on the market according to different stakeholders' groups in the European Union, based on the results of the impact assessment study realised for the evaluation of the General Product Safety Directive and its potential revision for the European Commission. The results were obtain from a survey conducted by Civic Consulting on 153 stakeholders (27 of consumer organisations and other general stakeholders, 48 of authorities, 37 of business associations and 41 of companies), who answer the question \"In your view, what is the best estimate of the share of unsafe products on the market in your area of activity (i.e. the estimated number of unsafe products per 100 products sold on the market)?\" The average values are calculated based on 100 (brick-and-mortar)/105 (online) stakeholders that had expressed an opinion (out of all respondents 53/48 indicated \"Don’t know\" or provided no answer). "},
{"data": [{"data": [9358360, 1651475], "name": "Share"}], "_data": [["Injuries", "Share"], ["Non-preventable product-related injuries", "9358360"], ["Preventable product-related injuries", "1651475"]], "labels": {"name": "Injuries", "values": ["Non-preventable product-related injuries", "Preventable product-related injuries"]}, "metadata": {"link": "https://ec.europa.eu/info/sites/default/files/study-part2-ia_1_1.pdf", "type": "Problem", "unit": "Number of injuries", "year": "2013-2017", "title": "The Proportion of Preventable Non-Fatal Product-Related Injuries To Total Non-Fatal Injuries (2013-2017)", "topic": "Illegal Products", "method": "Administrative data", "source": "European Commission. Study to Support the Preparation of an Evaluation of the General Product Safety Directive As Well As of an Impact Assessment on Its Potential Revision - Part 2: Impact Assessment (Brussels: European Commission, June 2021)", "sub_topic": "Product safety", "chart_number": "256", "geographical": "European Union"}, "description": "The chart presents the share of the preventable non-fatal product-related injuries in the European Union between 2013-2017, based on the results of the impact assessment study realised for the evaluation of the General Product Safety Directive and its potential revision for the European Commission. The average number of injuries are estimates based on the European Injury Database and include the number of accidental, non-intentional product-related injuries in which consumers visited hospital emergency departments. Data excludes transport injury events and work-related injuries (paid work). When it comes to the estimated prejudice caused by all product-related injuries to consumers in the European Union in 2017, 36.9 billion euros (or 48% of the total amount of 76.6 billion euros) was due to the non-fatal injuries, with preventable injuries accounting for 5.5 billion euros. The percentage of preventable non-fatal injuries is an estimate based on interviews and previous research and studies, including a 1999 study of the Accident Research Centre of University of Monash that, in turn, relied on data covering the period 1991-1992. "},
{"data": [{"data": [144864, 11196], "name": "Facebook"}, {"data": [69260, 3416373], "name": "Telegram"}, {"data": [389844, 19208], "name": "Twitter"}], "_data": [["Posts containing antisemitic content", "Facebook", "Telegram", "Twitter"], ["France", "144864", "69260", "389844"], ["Germany", "11196", "3416373", "19208"]], "labels": {"name": "Posts containing antisemitic content", "values": ["France", "Germany"]}, "metadata": {"link": "https://op.europa.eu/s/shTX", "type": "Problem", "unit": "Number of posts", "year": "2020-2021", "title": "Number of Posts Collected Containing Anti-Semitic Content", "topic": "Hate Speech", "method": "Data collection", "source": "European Commission. The Rise of Anti-Semitism Online During the Pandemic: A Study of French and German Content (Brussels: European Commission, April 2021)", "sub_topic": "Prelavalence of hate speech", "chart_number": "262", "geographical": "France, Germany"}, "description": "The chart presents the distribution of posts containg anti-semitic content across various social media platforms, based on the results of the European Commission \"The rise of anti-semitism online during the pandemic: A study of French and German content,\" prepared in 2021 and covering the period January 2020 - March 2021. The study aim to understand the impact of the Covid-19 pandemic might have had on the proliferation of the online anti-semitism and which platforms are particulary prone to anti-semitic messaging in French and German. The results show that 64.5% of French language posts with anti-semitic content are found on Twitter, while 99% of German language posts with anti-semitic content are on Telegram."},
{"data": [{"data": [97.9, 97.1, 97.5, 97.8], "name": "Percentage of content actioned that Facebook found and flagged before users reported it"}, {"data": [2.1, 2.9, 2.5, 2.2], "name": "Percentage of content actioned that users reported first"}], "_data": [["Period", "Percentage of content actioned that Facebook found and flagged before users reported it", "Percentage of content actioned that users reported first"], ["April-June 2021", "97.9", "2.1"], ["July-September 2021", "97.1", "2.9"], ["October-December 2021", "97.5", "2.5"], ["January-March 2022", "97.8", "2.2"]], "labels": {"name": "Period", "values": ["April-June 2021", "July-September 2021", "October-December 2021", "January-March 2022"]}, "metadata": {"link": "https://transparency.fb.com/data/community-standards-enforcement/child-nudity-and-sexual-exploitation/facebook/", "type": "Problem", "unit": "Per Cent (%)", "year": "2021-2022", "title": "Percentage of Content Found by Facebook as Containing Child Nudity and Physical Abuse Compared to the Content Reported by the Users", "topic": "Illegal Content", "method": "Self-reporting", "source": "Meta. Transparency Report: Child Endangerment: Nudity and Physical Abuse and Sexual Exploitation (June 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "263", "geographical": "Global"}, "description": "This chart shows the percentage of content found by Facebook as containing child nudity and physical abuse compared to the content reported by the users from April 2021 until March 2022. The percentage reported by users is significantly lower that the one found by Facebook."},
{"data": [{"data": [99.5, 99.1, 99, 96.4], "name": "Percentage of content actioned that Facebook found and flagged before users reported it"}, {"data": [0.5, 0.9, 1, 3.6], "name": "Percentage of content actioned that users reported first"}], "_data": [["Period", "Percentage of content actioned that Facebook found and flagged before users reported it", "Percentage of content actioned that users reported first"], ["April-June 2021", "99.5", "0.5"], ["July-September 2021", "99.1", "0.9"], ["October-December 2021", "99", "1"], ["January-March 2022", "96.4", "3.6"]], "labels": {"name": "Period", "values": ["April-June 2021", "July-September 2021", "October-December 2021", "January-March 2022"]}, "metadata": {"link": "https://transparency.fb.com/data/community-standards-enforcement/child-nudity-and-sexual-exploitation/facebook/", "type": "Problem", "unit": "Per Cent (%)", "year": "2021-2022", "title": "Percentage of Content Found by Facebook as Containing Child Sexual Exploitation Compared to the Content Reported by the Users", "topic": "Illegal Content", "method": "Self-reporting", "source": "Meta. Transparency Report: Child Endangerment: Nudity and Physical Abuse and Sexual Exploitation (June 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "264", "geographical": "Global"}, "description": "This chart shows the percentage of content found by Facebook as containing child sexual exploitation compared to the content reported by the users from April 2021 until March 2022. The percentage reported by users is significantly lower that the one found by Facebook."},
{"data": [{"data": [56, 14], "name": "Online sales"}, {"data": [44, 86], "name": "Not related to online sales"}], "_data": [["Period", "Online sales", "Not related to online sales"], ["Shares of detentions", "56", "44"], ["Share of value of seizures", "14", "86"]], "labels": {"name": "Period", "values": ["Shares of detentions", "Share of value of seizures"]}, "metadata": {"link": "https://www.oecd-ilibrary.org/governance/misuse-of-e-commerce-for-trade-in-counterfeits_1c04a64e-en", "type": "Problem", "unit": "Per Cent (%)", "year": "2017-2019", "title": "Distribution of Detentions and the Value of Seizures for Online and Not Related to Online Sales in European Union, 2017-2019", "topic": "Illegal Products", "method": "Data collection", "source": "OECD/EUIPO. Misuse of E-Commerce for Trade in Counterfeits, in Illicit Trade (Paris: OECD Publishing, 2021)", "sub_topic": "E-commerce", "chart_number": "265", "geographical": "European Union"}, "description": "This chart shows the share of detensions from online sales and their corresponding value of seisures in the European Union over the period 2017-2019, based on the results of the OECD/EUIPO report \"Misuse of E-Commerce for Trade in Counterfeits,\" published in October  2021. The results show that while the detentions related to online sales constitute majority of all the seizure observations registered between 2017 and 2019, their values remains much lower than the value of seizures from offline sales (14% compared to 86%)."},
{"data": [{"data": [71.9, 27.6, 18.4, 10, 8, 1.5], "name": "Online sales"}, {"data": [28.1, 72.4, 81.6, 90, 92, 98.5], "name": "Not related to online sales"}], "_data": [["Period", "Online sales", "Not related to online sales"], ["Mail/Post", "71.9", "28.1"], ["Air", "27.6", "72.4"], ["Rail", "18.4", "81.6"], ["Road/Vehicles", "10", "90"], ["Express courier", "8", "92"], ["Sea/Vessel", "1.5", "98.5"]], "labels": {"name": "Period", "values": ["Mail/Post", "Air", "Rail", "Road/Vehicles", "Express courier", "Sea/Vessel"]}, "metadata": {"link": "https://www.oecd-ilibrary.org/governance/misuse-of-e-commerce-for-trade-in-counterfeits_1c04a64e-en", "type": "Problem", "unit": "Per Cent (%)", "year": "2017-2019", "title": "Distribution of Detentions of Online Sales in Total Detentions in European Union, by Transport Mode, 2017-2019", "topic": "Illegal Products", "method": "Data collection", "source": "OECD/EUIPO. Misuse of E-Commerce for Trade in Counterfeits, in Illicit Trade (Paris: OECD Publishing, 2021)", "sub_topic": "E-commerce", "chart_number": "266", "geographical": "European Union"}, "description": "This chart shows the share of detentions of online sales in total detentions, by transport mode, in the European Union over the period 2017-2019, based on the results of the OECD/EUIPO report \"Misuse of E-Commerce for Trade in Counterfeits,\" published in October  2021. The results show that mail/post is the only transport mode of counterfeit goods where the number of detentions related with online sale is higher than the number of cases not related to online sale (71.9% of detentions concern online sales). All the other trasport modes do not exceed 30% of detentions concerning online sale, with Sea/Vessel having the lowest share (1.5%)."},
{"data": [{"data": [33.7, 17.3, 9.6, 8.7, 6.5, 5.5, 5.2, 3.4, 3.2, 1.6], "name": "Share of Detentions"}], "_data": [["Product category", "Share of Detentions"], ["Footware", "33.7"], ["Clothing, knitted or crocheted", "17.3"], ["Perfumery and cosmetics", "9.6"], ["Articles of leather", "8.7"], ["Electrical machinery and equipment", "6.5"], ["Toys", "5.5"], ["Watches", "5.2"], ["Optical, photographic and medical instruments", "3.4"], ["Vehicle parts", "3.2"], ["Pharmaceutical products", "1.6"]], "labels": {"name": "Product category", "values": ["Footware", "Clothing, knitted or crocheted", "Perfumery and cosmetics", "Articles of leather", "Electrical machinery and equipment", "Toys", "Watches", "Optical, photographic and medical instruments", "Vehicle parts", "Pharmaceutical products"]}, "metadata": {"link": "https://www.oecd-ilibrary.org/governance/misuse-of-e-commerce-for-trade-in-counterfeits_1c04a64e-en", "type": "Problem", "unit": "Per Cent (%)", "year": "2017-2019", "title": "Distribution of Detentions Related to Online Sales Between Product Categories in European Union, 2017-2019", "topic": "Illegal Products", "method": "Data collection", "source": "OECD/EUIPO. Misuse of E-Commerce for Trade in Counterfeits, in Illicit Trade (Paris: OECD Publishing, 2021)", "sub_topic": "E-commerce", "chart_number": "267", "geographical": "European Union"}, "description": "This chart shows the distribution of the detentions between product categories, in the context of online purcheses, in the European Union over the period 2017-2019, based on the results of the OECD/EUIPO report \"Misuse of E-Commerce for Trade in Counterfeits,\" published in October 2021. The results show that footwear and clothing are the product categories on top of the list of products with highest shares of detentions."},
{"data": [{"data": [35.2, 28.6, 23.3, 16.3, 15.6, 12.9, 8.5, 7.6, 5.6, 3.5], "name": "Share of Articles' Seized Value "}], "_data": [["Product category", "Share of Articles' Seized Value "], ["Vehicle parts", "35.2"], ["Pharmaceutical products", "28.6"], ["Watches", "23.3"], ["Jewellery", "16.3"], ["Articles of leather", "15.6"], ["Footware", "12.9"], ["Electrical machinery and equipment", "8.5"], ["Clothing, knitted or crocheted", "7.6"], ["Toys", "5.6"], ["Optical, photographic and medical instruments", "3.5"]], "labels": {"name": "Product category", "values": ["Vehicle parts", "Pharmaceutical products", "Watches", "Jewellery", "Articles of leather", "Footware", "Electrical machinery and equipment", "Clothing, knitted or crocheted", "Toys", "Optical, photographic and medical instruments"]}, "metadata": {"link": "https://www.oecd-ilibrary.org/governance/misuse-of-e-commerce-for-trade-in-counterfeits_1c04a64e-en", "type": "Problem", "unit": "Per Cent (%)", "year": "2017-2019", "title": "Distribution of Detentions Related to Online Sales Between Product Categories in European Union, by Value of Seized Articles, 2017-2019", "topic": "Illegal Products", "method": "Data collection", "source": "OECD/EUIPO. Misuse of E-Commerce for Trade in Counterfeits, in Illicit Trade (Paris: OECD Publishing, 2021)", "sub_topic": "E-commerce", "chart_number": "268", "geographical": "European Union"}, "description": "This chart shows the distribution of the share of value of seized products related to online transactions within each product category, in the European Union over the period 2017-2019, based on the results of the OECD/EUIPO report \"Misuse of E-Commerce for Trade in Counterfeits,\" published in October 2021. The results show that for three product categories: vehicles parts, pharmaceutical products and watches the value of seized counterfeit products purchased online exceeded 20% of value of all products seized within their respective categories."},
{"data": [{"data": [56, 72, 44, 46], "name": "Number of items"}], "_data": [["Items", "Number of items"], ["2017", "56"], ["2018", "72"], ["2019", "44"], ["2020", "46"]], "labels": {"name": "Items", "values": ["2017", "2018", "2019", "2020"]}, "metadata": {"link": "https://op.europa.eu/s/vMvD", "type": "Problem", "unit": "Number of items (millions)", "year": "2017-2020", "title": "Distribution of Quantity of Detained Items in the European Union Internal Market, 2017-2020", "topic": "Illegal Products", "method": "Data collection", "source": "European Union Intellectual Property Office. EU Enforcement of Intellectual Property Rights: Results at the EU Border and in the EU Internal Market 2020 (Alicante: EUIPO, 2021)", "sub_topic": "Counterfeit and pirated goods", "chart_number": "269", "geographical": "European Union"}, "description": "This chart shows the evolution of the number of fake items seized in the European Union Internal Market over the period 2017-2020, based on the results of the EUIPO report \"EU Enforcement of Intellectual Property Rights: Results at the EU Border and in the EU Internal Market 2020,\" published in December 2021. The results show that the number of fake items detained in the EU internal market in 2020 amounted to 46 million items, an increase of 3.6 % (or 1.6 million items) compared to 2019."},
{"data": [{"data": [1.4, 1.9, 1.8, 1.3], "name": "Annual Estimated Value"}], "_data": [["Detained items", "Annual Estimated Value"], ["2017", "1.4"], ["2018", "1.9"], ["2019", "1.8"], ["2020", "1.3"]], "labels": {"name": "Detained items", "values": ["2017", "2018", "2019", "2020"]}, "metadata": {"link": "https://op.europa.eu/s/vMvD", "type": "Problem", "unit": "Billions of euros", "year": "2017-2020", "title": "Estimated Value of Detained Items in the European Union Internal Market, 2017-2020", "topic": "Illegal Products", "method": "Data collection", "source": "European Union Intellectual Property Office. EU Enforcement of Intellectual Property Rights: Results at the EU Border and in the EU Internal Market 2020 (Alicante: EUIPO, 2021)", "sub_topic": "Counterfeit and pirated goods", "chart_number": "270", "geographical": "European Union"}, "description": "This chart shows the evolution of the value of seized products (billions of euros) in the European Union Internal Market over the period 2017-2020, based on the results of the EUIPO report \"EU Enforcement of Intellectual Property Rights: Results at the EU Border and in the EU Internal Market 2020,\" published in December 2021. The results show that the estimated value of these detained fake items amounted to almost 1.3 billion euros, a decrease of 27.3 % when compared with 2019."},
{"data": [{"data": [13396721, 8486416, 1968274, 1370017, 917720, 783025, null], "name": "2020"}], "_data": [["Detained items", "2020"], ["Sea", "13396721"], ["Road", "8486416"], ["Air", "1968274"], ["Express courier", "1370017"], ["Rail", "917720"], ["Post", "783025"], ["Waterway"]], "labels": {"name": "Detained items", "values": ["Sea", "Road", "Air", "Express courier", "Rail", "Post", "Waterway"]}, "metadata": {"link": "https://op.europa.eu/s/vMvD", "type": "Problem", "unit": "Number", "year": "2020", "title": "Distribution of Detained Items at the European Union Borders, by Means of Transport, 2020", "topic": "Illegal Products", "method": "Data collection", "source": "European Union Intellectual Property Office. EU Enforcement of Intellectual Property Rights: Results at the EU Border and in the EU Internal Market 2020 (Alicante: EUIPO, 2021)", "sub_topic": "Counterfeit and pirated goods", "chart_number": "271", "geographical": "European Union"}, "description": "This chart shows the distribution of the seized items by means of transports in the European Union in 2020, based on the results of the EUIPO report \"EU Enforcement of Intellectual Property Rights: Results at the EU Border and in the EU Internal Market 2020,\" published in December 2021. The results show that sea, road and air transport remain the most significant means of transport in terms of the number of articles detained, accounting for 88.6% of all items seized (23 million articles). "},
{"data": [{"data": [23.33, 20.48, 12.48, 7.82, 7.78, 5.98, 3.89, 2.59, 2.57, 1.76, 1.72, 1.56, 1.52, 1.12, 0.98, 0.65, 0.61, 0.45, 0.41, 0.28, 0.26, 0.23, 0.2, 0.19, 0.19, 0.18, 0.16, 0.16, 0.14, 0.12, 0.06, 0.05, 0.04, 0.03, 0.01, 0], "name": "2020"}], "_data": [["Detained items", "2020"], ["Watches", "23.33"], ["Clothing", "20.48"], ["Bags, wallets, purses", "12.48"], ["Sports shoes", "7.82"], ["Mobile phones accessories", "7.78"], ["Non-sports shoes", "5.98"], ["Jewellery", "3.89"], ["Toys", "2.59"], ["Perfumes and cosmetics", "2.57"], ["Clothing accessories", "1.76"], ["Mobile phones", "1.72"], ["Sunglasses", "1.56"], ["Audio/video apparatus", "1.52"], ["Vehicles accessories", "1.12"], ["Other goods", "0.98"], ["Lighters", "0.65"], ["Cigarettes", "0.61"], ["Games", "0.45"], ["Textiles", "0.41"], ["Packaging materials", "0.28"], ["Alcoholic beverages", "0.26"], ["Computer equipment", "0.23"], ["Foodstuffs", "0.20"], ["Machines/tools", "0.19"], ["Medicines", "0.19"], ["Other body care items", "0.18"], ["Sporting articles", "0.16"], ["Other electronics", "0.16"], ["Labels, tags, stickers", "0.14"], ["Other tobacco products", "0.12"], ["Other beverages", "0.06"], ["Recorded CDs/DVDs", "0.05"], ["Memory cards/sticks", "0.04"], ["Office stationery", "0.03"], ["Ink cartridges", "0.01"], ["Unrecorded CDs/DVDs", "0.00"]], "labels": {"name": "Detained items", "values": ["Watches", "Clothing", "Bags, wallets, purses", "Sports shoes", "Mobile phones accessories", "Non-sports shoes", "Jewellery", "Toys", "Perfumes and cosmetics", "Clothing accessories", "Mobile phones", "Sunglasses", "Audio/video apparatus", "Vehicles accessories", "Other goods", "Lighters", "Cigarettes", "Games", "Textiles", "Packaging materials", "Alcoholic beverages", "Computer equipment", "Foodstuffs", "Machines/tools", "Medicines", "Other body care items", "Sporting articles", "Other electronics", "Labels, tags, stickers", "Other tobacco products", "Other beverages", "Recorded CDs/DVDs", "Memory cards/sticks", "Office stationery", "Ink cartridges", "Unrecorded CDs/DVDs"]}, "metadata": {"link": "https://op.europa.eu/s/vMvD", "type": "Problem", "unit": "Per Cent (%)", "year": "2020", "title": "Distribution of Types of Products Detained at the European Union Borders, by Value, 2020", "topic": "Illegal Products", "method": "Data collection", "source": "European Union Intellectual Property Office. EU Enforcement of Intellectual Property Rights: Results at the EU Border and in the EU Internal Market 2020 (Alicante: EUIPO, 2021)", "sub_topic": "Counterfeit and pirated goods", "chart_number": "272", "geographical": "European Union"}, "description": "The chart shows the distribution of the share of types of products detained in total value of seized products, at the European Union border in 2020, based on the results of the EUIPO report \"EU Enforcement of Intellectual Property Rights: Results at the EU Border and in the EU Internal Market 2020,\" published in December 2021. The results show that, in terms of value, the top three product categories account for 56.3% of total estimated value of the sized items and they are the same ones as in the last two years - watches, clothing and bags, wallets and purses."},
{"data": [{"data": [15268, 191], "name": "2019"}], "_data": [["Product alerts", "2019"], ["Other alerted items", "15268.00"], ["Counterfeit or potentially counterfeit items", "191.00"]], "labels": {"name": "Product alerts", "values": ["Other alerted items", "Counterfeit or potentially counterfeit items"]}, "metadata": {"link": "https://euipo.europa.eu/tunnel-web/secure/webdav/guest/document_library/observatory/documents/reports/2019_Risks_Posed_by_Counterfeits_to_Consumers_Study/2019_Risks_Posed_by_Counterfeits_to_Consumers_Study.pdf", "type": "Problem", "unit": "Number", "year": "2010-2017", "title": "Counterfeit (or Potentially Counterfeit) Products out of Total Alerted Products, 2010 - 2017", "topic": "Illegal Products", "method": "Data collection", "source": "European Union Intellectual Property Office. Qualitative Study on Risks Posed by Counterfeits to Consumers (Alicante: EUIPO, 2019)", "sub_topic": "Counterfeit and pirated goods", "chart_number": "273", "geographical": "European Union"}, "description": "This chart shows the number of fake products identified as couterfeit or potentially counterfeit within the total number of alerted products, in the European Union over the period 2010-2017, based on the results of the EUIPO report \"Qualitative Study on Risks Posed by Counterfeits to Consumers,\" published in June 2019. The study uses the data from the European Commission Rapid Alert System For Dangerous Non-Food Products (RAPEX) reported by market surveillance authorities in the period 2010-2017. "},
{"data": [{"data": [57433, 69354, 91868, 69147], "name": "Number of Cases"}], "_data": [["Cases", "Number of Cases"], ["2017", "57433"], ["2018", "69354"], ["2019", "91868"], ["2020", "69147"]], "labels": {"name": "Cases", "values": ["2017", "2018", "2019", "2020"]}, "metadata": {"link": "https://op.europa.eu/s/vMvD", "type": "Problem", "unit": "Number", "year": "2017-2020", "title": "Cases of Products Detentions at the European Union Borders, 2017 - 2020", "topic": "Illegal Products", "method": "Data collection", "source": "European Union Intellectual Property Office. EU Enforcement of Intellectual Property Rights: Results at the EU Border and in the EU Internal Market 2020 (Alicante: EUIPO, 2021)", "sub_topic": "Counterfeit and pirated goods", "chart_number": "274", "geographical": "European Union"}, "description": "This chart shows the number of cases of products detentions at the European Union boders the period 2017-2020, based on the results of the EUIPO report \"EU Enforcement of Intellectual Property Rights: Results at the EU Border and in the EU Internal Market 2020,\" published in December 2021. The results show that the number of cases has declined in 2020 by almost 25%, reaching a similar level to the one in 2018. "},
{"data": [{"data": [31410703, 26720827, 40968254, 26922173], "name": "Number of Articles Detained"}], "_data": [["Cases", "Number of Articles Detained"], ["2017", "31410703"], ["2018", "26720827"], ["2019", "40968254"], ["2020", "26922173"]], "labels": {"name": "Cases", "values": ["2017", "2018", "2019", "2020"]}, "metadata": {"link": "https://op.europa.eu/s/vMvD", "type": "Problem", "unit": "Number", "year": "2017-2020", "title": "Number of Articles Seized at the European Union Borders, 2017 - 2020", "topic": "Illegal Products", "method": "Data collection", "source": "European Union Intellectual Property Office. EU Enforcement of Intellectual Property Rights: Results at the EU Border and in the EU Internal Market 2020 (Alicante: EUIPO, 2021)", "sub_topic": "Counterfeit and pirated goods", "chart_number": "275", "geographical": "European Union"}, "description": "The chart shows the number of articles detained at the European Union boders the period 2017-2020, based on the results of the EUIPO report \"EU Enforcement of Intellectual Property Rights: Results at the EU Border and in the EU Internal Market 2020,\" published in December 2021. The results show that the number of items seized in 2020 declined by 34% compared to 2019, reaching a level closer to the 2018 one. "},
{"data": [{"data": [5.1, 6.8, 5.8], "name": "Counterfeit and pirated goods trade (%) "}], "_data": [["Cases", "Counterfeit and pirated goods trade (%) "], ["2013", "5.1"], ["2016", "6.8"], ["2019", "5.8"]], "labels": {"name": "Cases", "values": ["2013", "2016", "2019"]}, "metadata": {"link": "https://doi.org/10.1787/74c81154-en", "type": "Problem", "unit": "Per cent (%)", "year": "2013, 2016, 2019", "title": "Estimates of Counterfeit and Pirated Goods to the European Union in 2013, 2016 and 2019", "topic": "Illegal Products", "method": "Data collection", "source": "OECD/EUIPO. Trends in Trade in Counterfeit and Pirated Goods Reports, Illicit Trade (Paris: OECD Publishing, 2016, 2019, 2021)", "sub_topic": "Counterfeit and pirated goods", "chart_number": "276", "geographical": "European Union"}, "description": "The chart shows the share of estimated counterfeit and pirated goods trade in the European Union imports, for the years 2013, 2016 and 2019, based on the results of the OECD/EUIPO reports \"Trade in Counterfeit and Pirated Goods,\" published in 2016, 2019 and 2021. The results show a decline by one per cent in the share of trade in the counterfeit and pirated goods in EU imports in 2019 compared to 2016."},
{"data": [{"data": [42025, 39045, 50454, 52219, 66529, 52943, 29939, 28331], "name": "User"}, {"data": [16272, 15599, 20714, 19688, 24285, 20534, 18218, 15516], "name": "Agency"}], "_data": [["Period", "User", "Agency"], ["January - June 2018", "42025", "16272"], ["July - December 2018", "39045", "15599"], ["January - June 2019", "50454", "20714"], ["July - December 2019", "52219", "19688"], ["January - June 2020", "66529", "24285"], ["July - December 2020", "52943", "20534"], ["January - June 2021", "29939", "18218"], ["July - December 2021", "28331", "15516"]], "labels": {"name": "Period", "values": ["January - June 2018", "July - December 2018", "January - June 2019", "July - December 2019", "January - June 2020", "July - December 2020", "January - June 2021", "July - December 2021"]}, "metadata": {"link": "https://transparencyreport.google.com/netzdg/youtube?community_guidelines_enforcement=period:Y2019H1&lu=reports_resulting_in_action&items_by_reason=period:Y2018H1&reports_resulting_in_action=period:Y2018H1", "type": "Solution", "unit": "Number of items removed", "year": "2018-2021", "title": "Content Removal Under the Germany’s Network Enforcement Act (2018-2021)", "topic": "Illegal Content", "method": "Self-reported", "source": "Google. Transparency Report: Removals under the Network Enforcement Law (www.google.com, 2022)", "sub_topic": "Removal of illegal content", "chart_number": "277", "geographical": "Germany"}, "description": "The chart shows the total number of items removed or blocked by Google, due to violations of the Germany’s Network Enforcement Act, by the type of submitter (users and reporting agencies). The results are based on the data from Google Transparency Report, last accessed on 14 February 2022."},
{"data": [{"data": [24.95, 22.19, 15.82, 22.8, 24.34, 25.76, 15.61], "name": "Removal of Reported Content"}], "_data": [["Reason", "Removal of Reported Content"], ["Privacy", "24.95"], ["Defamation or insults", "22.19"], ["Harmful or dangerous acts", "15.82"], ["Sexual content", "22.80"], ["Terrorist or unconstitutional content", "24.34"], ["Hate speech or political extremism", "25.76"], ["Violence", "15.61"]], "labels": {"name": "Reason", "values": ["Privacy", "Defamation or insults", "Harmful or dangerous acts", "Sexual content", "Terrorist or unconstitutional content", "Hate speech or political extremism", "Violence"]}, "metadata": {"link": "https://transparencyreport.google.com/netzdg/youtube?community_guidelines_enforcement=period:Y2021H2&lu=community_guidelines_enforcement", "type": "Solution", "unit": "Per Cent (%)", "year": "2018-2021", "title": "Share of Reported Content Removed by Google Under Germany’s Network Enforcement Act, by Reason of Removal (2018-2021)", "topic": "Illegal Content", "method": "Self-reporting", "source": "Google. Transparency Report: Removals under the Network Enforcement Law (www.google.com, 2022)", "sub_topic": "Removal of illegal content", "chart_number": "278", "geographical": "Germany"}, "description": "The chart presents the share of the reported content removed or blocked by Google due to violation of the Germany’s Network Enforcement Act, over the period 2018-2021, by reason of removal. The data shows that hate speech or political extremism, privacy and terrorist or unconstitutional content have the highest share of removals, while violence has the lowest one."},
{"data": [{"data": [6182263, 6441778, 6194346, 6387658, 6190148, 6372936, 7833499, 8163067, 5344863, 5711586, 10849634, 7390963, 8800082, 9091315, 5927201, 5901241, 3451691, 3544195], "name": "Automated flagging"}, {"data": [1159861, 2203306, 635745, 878273, 1942913, 1396945, 783228, 345435, 327876, 300407, 382499, 340694, 354429, 362110, 296454, 233349, 232737, 292715], "name": "User"}, {"data": [409747, 491318, 302939, 547142, 603696, 520430, 365339, 253178, 202105, 91203, 167318, 139408, 165180, 114061, 54339, 85791, 59240, 42022], "name": "Trusted Flagger"}, {"data": [10491, 17900, 24425, 32293, 28974, 4022, 6742, 4203, 12141, 7774, 2220, 1580, 2217, 2069, 656, 9471, 10531, 3721], "name": "NGO"}, {"data": [69, 49, 49, 34, 54, 16, 12, 10, 36, 38, 25, 39, 40, 86, 121, 30, 16, 31], "name": "Government Agency"}], "_data": [["Period", "Automated flagging", "User", "Trusted Flagger", "NGO", "Government Agency"], ["October - December 2017", "6182263", "1159861", "409747", "10491", "69"], ["January - March 2018", "6441778", "2203306", "491318", "17900", "49"], ["April - June 2018", "6194346", "635745", "302939", "24425", "49"], ["July - September 2018", "6387658", "878273", "547142", "32293", "34"], ["October - December 2018", "6190148", "1942913", "603696", "28974", "54"], ["January - March 2019", "6372936", "1396945", "520430", "4022", "16"], ["April - June 2019", "7833499", "783228", "365339", "6742", "12"], ["July - September 2019", "8163067", "345435", "253178", "4203", "10"], ["October - December 2019", "5344863", "327876", "202105", "12141", "36"], ["January - March 2020", "5711586", "300407", "91203", "7774", "38"], ["April - June 2020", "10849634", "382499", "167318", "2220", "25"], ["July - September 2020", "7390963", "340694", "139408", "1580", "39"], ["October - December 2020", "8800082", "354429", "165180", "2217", "40"], ["January - March 2021", "9091315", "362110", "114061", "2069", "86"], ["April-June 2021", "5927201", "296454", "54339", "656", "121"], ["July-September 2021", "5901241", "233349", "85791", "9471", "30"], ["October - December 2021", "3451691", "232737", "59240", "10531", "16"], ["January - March 2022", "3544195", "292715", "42022", "3721", "31"]], "labels": {"name": "Period", "values": ["October - December 2017", "January - March 2018", "April - June 2018", "July - September 2018", "October - December 2018", "January - March 2019", "April - June 2019", "July - September 2019", "October - December 2019", "January - March 2020", "April - June 2020", "July - September 2020", "October - December 2020", "January - March 2021", "April-June 2021", "July-September 2021", "October - December 2021", "January - March 2022"]}, "metadata": {"link": "https://transparencyreport.google.com/youtube-policy/removals?hl=en", "type": "Problem", "unit": "Number of removed videos ", "year": "2017-2022", "title": "The Number of Videos Removed by YouTube, by Source of First Detection", "topic": "Illegal Content", "method": "Self-reporting", "source": "Google. Transparency Report: YouTube Community Guidelines Enforcement (www.google.com, 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "279", "geographical": "Global"}, "description": "The chart number of videos removed by YouTube for the period October 2017-March 2022, by first source of detection (automated flagging or human detection). Flags from human detection can come from a user or a member of YouTube’s Trusted Flagger program,which include individuals, NGOs, and government agencies. The chart shows that the number of automated flagging is significantly higher compared to human detection. When it comes to human detection, the biggest number of removed videos were first noticed by users, followed by individual trusted flaggers, NGOs and government agencies."},
{"data": [{"data": [1084552571, 20082262, 326111], "name": "Values"}], "_data": [["Human Flags", "Values"], ["User", "1084552571"], ["Trusted Flagger", "20082262"], ["NGO", "326111"]], "labels": {"name": "Human Flags", "values": ["User", "Trusted Flagger", "NGO"]}, "metadata": {"link": "https://transparencyreport.google.com/youtube-policy/flags?hl=en", "type": "Problem", "unit": "Per cent (%)", "year": "2017-2022", "title": "Human Flags of YouTube Videos by Type of Flagger", "topic": "Illegal Content", "method": "Self-reporting", "source": "Google. Transparency Report: Flags - Illegal Content Online (www.google.com, 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "280", "geographical": "Global"}, "description": "The chart shows the distribution of human flags on YouTube for the period October 2017 - March 2022, by type of flagger. Human flags can come from a user or a member of YouTube’s Trusted Flagger program,which include individuals, NGOs and government agencies. The chart shows that the majority of human flags come from users, followed by individual trusted flaggers. The share of flags from NGOs is insignificant compared to the other two type of flaggers."},
{"data": [{"data": [28708961, 36080146, 37431698, 40542766, 41758590, 45243228, 42413219, 47102766, 47319095, 54054064, 77239233, 87240568, 84504591, 86663205, 90601891, 84975527, 74752570, 77920453], "name": "User"}, {"data": [1344890, 2973963, 517121, 1405415, 2747520, 3303352, 2611511, 1909603, 535229, 406846, 360997, 333553, 436475, 302520, 256397, 221510, 217504, 197856], "name": "Individual Trusted Flagger"}, {"data": [14533, 26062, 36848, 52594, 37525, 6817, 5404, 7143, 17813, 11739, 5045, 3524, 5826, 2886, 2713, 44209, 39781, 5649], "name": "NGO"}], "_data": [["Period", "User", "Individual Trusted Flagger", "NGO"], ["October - December 2017", "28708961", "1344890", "14533"], ["January - March 2018", "36080146", "2973963", "26062"], ["April - June 2018", "37431698", "517121", "36848"], ["July - September 2018", "40542766", "1405415", "52594"], ["October - December 2018", "41758590", "2747520", "37525"], ["January - March 2019", "45243228", "3303352", "6817"], ["April - June 2019", "42413219", "2611511", "5404"], ["July - September 2019", "47102766", "1909603", "7143"], ["October - December 2019", "47319095", "535229", "17813"], ["January - March 2020", "54054064", "406846", "11739"], ["April - June 2020", "77239233", "360997", "5045"], ["July - September 2020", "87240568", "333553", "3524"], ["October - December 2020", "84504591", "436475", "5826"], ["January - March 2021", "86663205", "302520", "2886"], ["April-June 2021", "90601891", "256397", "2713"], ["July-September 2021", "84975527", "221510", "44209"], ["October - December 2021", "74752570", "217504", "39781"], ["January-March 2022", "77920453", "197856", "5649"]], "labels": {"name": "Period", "values": ["October - December 2017", "January - March 2018", "April - June 2018", "July - September 2018", "October - December 2018", "January - March 2019", "April - June 2019", "July - September 2019", "October - December 2019", "January - March 2020", "April - June 2020", "July - September 2020", "October - December 2020", "January - March 2021", "April-June 2021", "July-September 2021", "October - December 2021", "January-March 2022"]}, "metadata": {"link": "https://transparencyreport.google.com/youtube-policy/flags?hl=en", "type": "Problem", "unit": "Per cent (%)", "year": "2017-2022", "title": "Human Flags of YouTube Videos by Type of Flagger", "topic": "Illegal Content", "method": "Self-reporting", "source": "Google. Transparency Report: Flags - Illegal Content Online (www.google.com, 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "281", "geographical": "Global"}, "description": "The chart shows the distribution of human flags on YouTube for the period October 2017 - March 2022, by type of flagger. Human flags can come from a user or a member of YouTube’s Trusted Flagger program,which include individuals, NGOs, and government agencies. The chart shows that the majority of human flags come from users, followed by individual trusted flaggers. The share of flags from NGOs is insignificant compared to the other two type of flaggers."},
{"data": [{"data": [86.5, 40, 3.1, 40.1, 100, 13.2], "name": "General user"}, {"data": [88.2, 76.6, 25.3, 59.6, null, 93.7], "name": "Trusted flagger/Reporter"}], "_data": [["Online Platform", "General user", "Trusted flagger/Reporter"], ["Facebook", "86.5", "88.2"], ["Twitter", "40", "76.6"], ["YouTube", "3.1", "25.3"], ["Instagram", "40.1", "59.6"], ["Jeuxvideo.com", "100"], ["TikTok", "13.2", "93.7"]], "labels": {"name": "Online Platform", "values": ["Facebook", "Twitter", "YouTube", "Instagram", "Jeuxvideo.com", "TikTok"]}, "metadata": {"link": "https://ec.europa.eu/info/sites/default/files/factsheet-6th-monitoring-round-of-the-code-of-conduct_october2021_en_1.pdf", "type": "Solution", "unit": "Per Cent (%)", "year": "2021", "Range": "0 100", "title": "Feedback Provided by Online Platforms to Different Types of User (2021)", "topic": "Hate Speech", "method": "Self-reporting", "source": "European Commission. Sixth Evaluation on the Code of Conduct on Countering Illegal Hate Speech Online (Brussels: European Commission, 2021)  ", "sub_topic": "Removal of hate speech", "chart_number": "282", "geographical": "European Union"}, "description": "This chart shows the per cent of feedback provided by online platforms to different types of users (general user or trusted flagger/reporter). The results are based on data reported by social media platforms participating in the European Commission's Code of conduct. The data shows that Facebook is informing consistently both trusted flaggers and general users, while Twitter, YouTube, TikTok and Instagram provide feedback more frequently when notifications come from trusted flaggers. Jeuxvideo has significantly increased its performance on feedback to users (it was 22.5% in 2020). One of the conclusions included in the sixth monitoring exercice is that notifications from general users continue to be often treated differently than those sent through special channels for “trusted flaggers”, with differences varying from 1.7% (Facebook) to 80% (TikTok)."},
{"data": [{"data": [18.2, 18, 12.5, 9.3, 8.5, 7.7, 5.8, 5.1, 4.7, 4.3, 3.9, 2], "name": "Per cent (%)"}], "_data": [["Type", "Per cent (%)"], ["Sexual orientation", "18.2"], ["Xenophobia (including anti-migrant hatred)", "18"], ["Anti-gypsyism", "12.5"], ["Antisemitism", "9.3"], ["Anti-Muslim hatred", "8.5"], ["Afrophobia", "7.7"], ["National origin", "5.8"], ["Gender-based hate speech", "5.1"], ["Other", "4.7"], ["Ethnic origin", "4.3"], ["Race", "3.9"], ["Religion", "2"]], "labels": {"name": "Type", "values": ["Sexual orientation", "Xenophobia (including anti-migrant hatred)", "Anti-gypsyism", "Antisemitism", "Anti-Muslim hatred", "Afrophobia", "National origin", "Gender-based hate speech", "Other", "Ethnic origin", "Race", "Religion"]}, "metadata": {"link": "https://ec.europa.eu/info/sites/default/files/factsheet-6th-monitoring-round-of-the-code-of-conduct_october2021_en_1.pdf", "type": "Solution", "unit": "Per Cent (%)", "year": "2021", "title": "Grounds of Hatred Reported by Social Media Platforms (2021)", "topic": "Hate Speech", "method": "Self-reporting", "source": "European Commission. Sixth Evaluation on the Code of Conduct on Countering Illegal Hate Speech Online (Brussels: European Commission, 2021)  ", "sub_topic": "Removal of hate speech", "chart_number": "283", "geographical": "European Union"}, "description": "The chart shows the grounds of hatred reported for reviewed posts, based on data reported by social media platforms participating in the European Commission's Code of conduct. In 2021, sexual orientation and xenophobia were the most common grounds for hatred, while religion, race and ethnic origin were the least common grounds for hatred. (Note: The data on grounds of hatred are only an indication and are influenced by the number of notifications sent by each organisation as well as their field of work.)"},
{"data": [{"data": [1093, 502, 952, 659, 761, 1014, 3225, 2890, 3620, 3074, 7363, 6407, 9499, 2835, 1614, 589], "name": "European Union"}], "_data": [["Period", "European Union"], ["January 2021", "1093"], ["February 2021", "502"], ["March 2021", "952"], ["April 2021", "659"], ["May 2021", "761"], ["June 2021", "1014"], ["July 2021", "3225"], ["August 2021", "2890"], ["September 2021", "3620"], ["October 2021", "3074"], ["November 2021", "7363"], ["December 2021", "6407"], ["January 2022", "9499"], ["February 2022", "2835"], ["March 2022", "1614"], ["April 2022", "589"]], "labels": {"name": "Period", "values": ["January 2021", "February 2021", "March 2021", "April 2021", "May 2021", "June 2021", "July 2021", "August 2021", "September 2021", "October 2021", "November 2021", "December 2021", "January 2022", "February 2022", "March 2022", "April 2022"]}, "metadata": {"link": "https://digital-strategy.ec.europa.eu/en/library/reports-march-and-april-actions-fighting-covid-19-disinformation", "type": "Solution", "unit": "Number of videos removed", "year": "2021-2022", "title": "Number of Videos Removed Containing Medical Misinformation on TikTok", "topic": "Disinformation", "method": "Self-reporting", "source": "TikTok. March and April 2022 Report EU Code of Practice on Disinformation / COVID-19 (May 2022)", "sub_topic": "Removal of disinformation", "chart_number": "284", "geographical": "European Union"}, "description": "The chart presents the distribution of videos removed from TikTok, found in violation of the community guidelines, for the period January 2021 to April 2022. The report shows the efforts of TikTok to limit the spread of COVID-19 disinformation online and it is part of the European Commission's Code of Practice on Disinformation monitoring process. The data covers all European Union countries."},
{"data": [{"data": [3303, 1744, 1941, 1455, 1211, 1012, 949, 1082, 686, 584, 722, 1247, 2291, 974, 637, 389], "name": "European Union"}], "_data": [["Period", "European Union"], ["January 2021", "3303"], ["February 2021", "1744"], ["March 2021", "1941"], ["April 2021", "1455"], ["May 2021", "1211"], ["June 2021", "1012"], ["July 2021", "949"], ["August 2021", "1082"], ["September 2021", "686"], ["October 2021", "584"], ["November 2021", "722"], ["December 2021", "1247"], ["January 2022", "2291"], ["February 2022", "974"], ["March 2022", "637"], ["April 2022", "389"]], "labels": {"name": "Period", "values": ["January 2021", "February 2021", "March 2021", "April 2021", "May 2021", "June 2021", "July 2021", "August 2021", "September 2021", "October 2021", "November 2021", "December 2021", "January 2022", "February 2022", "March 2022", "April 2022"]}, "metadata": {"link": "https://digital-strategy.ec.europa.eu/en/library/reports-january-and-february-actions", "type": "Solution", "unit": "Number of videos removed", "year": "2021-2022", "title": "Number of Videos Removed Containing the Term “Coronavirus” or “Covid” Found in Violation of TikTok Policy", "topic": "Disinformation", "method": "Self-reporting", "source": "TikTok. March and April 2022 Report EU Code of Practice on Disinformation / COVID-19 (May 2022)", "sub_topic": "Removal of disinformation", "chart_number": "285", "geographical": "European Union"}, "description": "The chart presents the distribution of videos removed from TikTok, found in violation of the community guidelines, for the period January 2021 to April 2022. The report shows the efforts of TikTok to limit the spread of COVID-19 disinformation online and it is part of the European Commission's Code of Practice on Disinformation monitoring process. The data covers all European Union countries."},
{"data": [{"data": [1007, 315, 497, 1338, 166, 523, 7186, 6822, 5371, 5091, 5147, 5117, 6602, 5644, 4544, 3574, 4129, 4559, 3397, 1828, 1392, 5320], "name": "Total"}], "_data": [["Period", "Total"], ["July 2020", "1007"], ["August 2020", "315"], ["September 2020", "497"], ["October 2020", "1338"], ["November 2020", "166"], ["December 2020", "523"], ["January 2021", "7186"], ["February 2021", "6822"], ["March 2021", "5371"], ["April 2021", "5091"], ["May 2021", "5147"], ["June 2021", "5117"], ["July 2021", "6602"], ["August 2021", "5644"], ["September 2021", "4544"], ["October 2021", "3574"], ["November 2021", "4129"], ["December 2021", "4559"], ["January 2022", "3397"], ["February 2022", "1828"], ["March 2022", "1392"], ["April 2022", "5320"]], "labels": {"name": "Period", "values": ["July 2020", "August 2020", "September 2020", "October 2020", "November 2020", "December 2020", "January 2021", "February 2021", "March 2021", "April 2021", "May 2021", "June 2021", "July 2021", "August 2021", "September 2021", "October 2021", "November 2021", "December 2021", "January 2022", "February 2022", "March 2022", "April 2022"]}, "metadata": {"link": "https://digital-strategy.ec.europa.eu/en/library/reports-march-and-april-actions-fighting-covid-19-disinformation", "type": "Solution", "unit": "Pieces of content removed", "year": "2020-2022", "title": "Content Removed for Violations of Twitter COVID-19 Misleading Information Policy", "topic": "Disinformation", "method": "Self-reporting", "source": "Twitter. Twitter Report on COVID-19 Misinformation (May 2022)", "sub_topic": "Removal of disinformation", "chart_number": "286", "geographical": "Global"}, "description": "The chart presents the distribution of the content removed by Twitter, due to violations of its COVID-19 Misleading Information Policy, for the period July 2020 - April 2022. The report shows the efforts of Twitter to limit the spread of COVID-19 disinformation online and it is part of the European Commission's Code of Practice on Disinformation monitoring process."},
{"data": [{"data": [23000, 31000, 36000, 100000, 28000, 35000, 10000, 13000, 52000, 47000, 62000, 76000, 110000, 160000, 120000, 140000, 110000, 109000, 149800, 76000, 41000, 46800], "name": "European Union countries"}, {"data": [null, null, null, null, 782000, 325000, 391000, 277000, 568000, 1253000, 868000, 634000, 770000, 940000, 830000, 540000, 260000, 351000, 685200, 444000, 339000, 401200], "name": "Other countries"}], "_data": [["Period", "European Union countries", "Other countries"], ["June 2020", "23000"], ["July 2020", "31000"], ["August 2020", "36000"], ["September 2020", "100000"], ["October 2020", "28000", "782000"], ["November 2020", "35000", "325000"], ["December 2020", "10000", "391000"], ["January 2021", "13000", "277000"], ["March 2021", "52000", "568000"], ["April 2021", "47000", "1253000"], ["May 2021", "62000", "868000"], ["June 2021", "76000", "634000"], ["July 2021", "110000", "770000"], ["August 2021", "160000", "940000"], ["September 2021", "120000", "830000"], ["October 2021", "140000", "540000"], ["November 2021", "110000", "260000"], ["December 2021", "109000", "351000"], ["January 2022", "149800", "685200"], ["February 2022", "76000", "444000"], ["March 2022", "41000", "339000"], ["April 2022", "46800", "401200"]], "labels": {"name": "Period", "values": ["June 2020", "July 2020", "August 2020", "September 2020", "October 2020", "November 2020", "December 2020", "January 2021", "March 2021", "April 2021", "May 2021", "June 2021", "July 2021", "August 2021", "September 2021", "October 2021", "November 2021", "December 2021", "January 2022", "February 2022", "March 2022", "April 2022"]}, "metadata": {"link": "https://digital-strategy.ec.europa.eu/en/library/reports-march-and-april-actions-fighting-covid-19-disinformation", "type": "Solution", "unit": "Pieces of content removed", "year": "2020-2022", "title": "Content Removed for Violations of Meta COVID-19 and Vaccine Misinformation Policies", "topic": "Disinformation", "method": "Self-reporting", "source": "Meta. Fighting COVID-19 Disinformation (May 2022)", "sub_topic": "Removal of disinformation", "chart_number": "287", "geographical": "Global"}, "description": "The chart presents the distribution of the content removed by Meta from Facebook and Instagram, due to violations of its COVID-19 Misleading Information Policy, for the period June 2020 - April 2022. The report shows the efforts of Meta to limit the spread of COVID-19 disinformation online and it is part of the European Commission's Code of Practice on Disinformation monitoring process."},
{"data": [{"data": [50.38, 22.39, 10.8, 10.22, 5.13, 1.08], "name": "Share"}], "_data": [["Country", "Share"], ["China", "50.38"], ["Greece", "22.39"], ["Hong Kong, China", "10.8"], ["Turkey", "10.22"], ["Other countries", "5.13"], ["Jordan", "1.08"]], "labels": {"name": "Country", "values": ["China", "Greece", "Hong Kong, China", "Turkey", "Other countries", "Jordan"]}, "metadata": {"link": "https://www.europol.europa.eu/cms/sites/default/files/documents/Report.%20Intellectual%20property%20crime%20threat%20assessment%202022_2.pdf", "type": "Solution", "unit": "Per cent (%)", "year": "2020", "title": "Countries of Provenance of Counterfeit Items Seized at the European Union Borders by Number of Articles (2020)", "topic": "Illegal Products", "method": "Data collection", "source": "EUIPO, Europol. Intellectual Property Crime: Threat Assessment 2022 (Alicante: EUIPO, 2022) ", "sub_topic": "Counterfeit and pirated goods", "chart_number": "288", "geographical": "European Union"}, "description": "The chart presents the distribution of the countries of provenience for the counterfeit items seized at the European Union external border in 2020, by number of articles detained, based on the results of the report \"Intellectual Property Crime: Threat Assessment 2022,\" published in March 2022 by EUIPO and Europol. The data shows that China (including Hong Kong) was the main country of origin for IPR-infringing goods seized at the EU’s external border, followed by Greece and Turkey."},
{"data": [{"data": [45.02, 23.73, 19.22, 6.32, 4.02, 1.69], "name": "Share"}], "_data": [["Country", "Share"], ["China", "45.02"], ["Hong Kong, China", "23.73"], ["Turkey", "19.22"], ["Singapore", "6.32"], ["Other countries", "4.02"], ["Saudi Arabia", "1.69"]], "labels": {"name": "Country", "values": ["China", "Hong Kong, China", "Turkey", "Singapore", "Other countries", "Saudi Arabia"]}, "metadata": {"link": "https://www.europol.europa.eu/cms/sites/default/files/documents/Report.%20Intellectual%20property%20crime%20threat%20assessment%202022_2.pdf", "type": "Solution", "unit": "Per cent (%)", "year": "2020", "title": "Countries of Provenance of Counterfeit Items Seized at the European Union Borders by Value (2020)", "topic": "Illegal Products", "method": "Data collection", "source": "EUIPO, Europol. Intellectual Property Crime: Threat Assessment 2022 (Alicante: EUIPO, 2022) ", "sub_topic": "Counterfeit and pirated goods", "chart_number": "289", "geographical": "European Union"}, "description": "The chart presents the distribution of the countries of provenience for the counterfeit items detained at the European Union external border in 2020, by the value of seizures, based on the results of the report \"Intellectual Property Crime: Threat Assessment 2022,\" published in March 2022 by EUIPO and Europol. The data shows that China (including Hong Kong) was the main country of origin for IPR-infringing goods seized at the EU’s external border, followed by Turkey and Singapore."},
{"data": [{"data": [56, 72, 44, 46], "name": "In the Internal Market"}, {"data": [24, 21, 32, 20], "name": "At EU borders"}], "_data": [["Country", "In the Internal Market", "At EU borders"], ["2017", "56", "24"], ["2018", "72", "21"], ["2019", "44", "32"], ["2020", "46", "20"]], "labels": {"name": "Country", "values": ["2017", "2018", "2019", "2020"]}, "metadata": {"link": "https://www.europol.europa.eu/cms/sites/default/files/documents/Report.%20Intellectual%20property%20crime%20threat%20assessment%202022_2.pdf", "type": "Solution", "unit": "Number of items (millions)", "year": "2017-2020", "title": "Number of Counterfeit Items Seized by the European Union Authorities", "topic": "Illegal Products", "method": "Data collection", "source": "EUIPO, Europol. Intellectual Property Crime: Threat Assessment 2022 (Alicante: EUIPO, 2022) ", "sub_topic": "Counterfeit and pirated goods", "chart_number": "290", "geographical": "European Union"}, "description": "The chart presents the number of counterfeit items detained by the European Union authorities over the period 2017 - 2020, based on the results of the report \"Intellectual Property Crime: Threat Assessment 2022,\" published in March 2022 by EUIPO and Europol. The data shows that, in 2020, approximately 66 million counterfeit items were seized by authorities in the EU, with 69.7% of them seized in the EU internal market and 30.3% at the EU borders. Compared to 2019, the volume of counterfeit items detained in 2020 declined by 13%."},
{"data": [{"data": [1.9, 2.6, 2.5, 2], "name": "Total"}], "_data": [["Country", "Total"], ["2017", "1.9"], ["2018", "2.6"], ["2019", "2.5"], ["2020", "2"]], "labels": {"name": "Country", "values": ["2017", "2018", "2019", "2020"]}, "metadata": {"link": "https://www.europol.europa.eu/cms/sites/default/files/documents/Report.%20Intellectual%20property%20crime%20threat%20assessment%202022_2.pdf", "type": "Solution", "unit": "Billion of euros", "year": "2017-2020", "title": "Estimated Value of Counterfeit Items Seized by the European Union Authorities", "topic": "Illegal Products", "method": "Data collection", "source": "EUIPO, Europol. Intellectual Property Crime: Threat Assessment 2022 (Alicante: EUIPO, 2022) ", "sub_topic": "Counterfeit and pirated goods", "chart_number": "291", "geographical": "European Union"}, "description": "The chart presents the estimated value of the counterfeit items detained by the European Union authorities over the period 2017 - 2020, based on the results of the report \"Intellectual Property Crime: Threat Assessment 2022,\" published in March 2022 by EUIPO and Europol. The data shows that the estimated value of the counterfeit items seized decreased by almost 19%, from almost 2.5 billion euros in 2019 to 2 billion euros in 2020."},
{"data": [{"data": [190, 122, 360, 550, 1346, 1203], "name": "Total seizures"}], "_data": [["Country", "Total seizures"], ["2015 <br /> 7 countries ", "190"], ["2017 <br /> 16 countries ", "122"], ["2018 <br /> 27 countries ", "360"], ["2019 <br /> 29 countries ", "550"], ["2020 <br /> 32 countries ", "1346"], ["2021 <br /> 35 countries ", "1203"]], "labels": {"name": "Country", "values": ["2015 <br /> 7 countries ", "2017 <br /> 16 countries ", "2018 <br /> 27 countries ", "2019 <br /> 29 countries ", "2020 <br /> 32 countries ", "2021 <br /> 35 countries "]}, "metadata": {"link": "https://www.europol.europa.eu/cms/sites/default/files/documents/Report.%20Intellectual%20property%20crime%20threat%20assessment%202022_2.pdf", "type": "Solution", "unit": "Tonnes", "year": "2017-2020", "title": "Tonnes of Illicit Pesticide Seized by the European Union Authorities", "topic": "Illegal Products", "method": "Data collection", "source": "EUIPO, Europol. Intellectual Property Crime: Threat Assessment 2022 (Alicante: EUIPO, 2022) ", "sub_topic": "Counterfeit and pirated goods", "chart_number": "292", "geographical": "European Union"}, "description": "The chart presents the amount of illicit pesticides seized by the European Union authorities, during the Operation Silver Axe, over the period 2015 - 2021, based on the results of the report \"Intellectual Property Crime: Threat Assessment 2022,\" published in March 2022 by EUIPO and Europol. The data shows that during the six editions of Operation Silver Axe 3,771 tonnes of illegal pesticides were seized, out of which 2,549 tones were seized in the last two editions (2020 and 2021)."},
{"data": [{"data": [5.952, 0.006], "name": "Facebook (Likes, Comments, Shares)"}, {"data": [54.828, 99.993], "name": "Telegram (Views)"}, {"data": [39.219, 0.001], "name": "Twitter (Retweets, Favourites)"}], "_data": [["Anti-semitic posts", "Facebook (Likes, Comments, Shares)", "Telegram (Views)", "Twitter (Retweets, Favourites)"], ["France", "5.952", "54.828", "39.219"], ["Germany", "0.006", "99.993", "0.001"]], "labels": {"name": "Anti-semitic posts", "values": ["France", "Germany"]}, "metadata": {"link": "https://op.europa.eu/s/shTX", "type": "Problem", "unit": "Per cent (%)", "year": "2020-2021", "title": "Engagement with Anti-Semitic Posts", "topic": "Hate Speech", "method": "Data collection", "source": "European Commission. The Rise of Anti-Semitism Online During the Pandemic: A Study of French and German Content (Brussels: European Commission, April 2021)", "sub_topic": "Prelavalence of hate speech", "chart_number": "293", "geographical": "France, Germany"}, "description": "The chart presents the distribution of posts containg anti-semitic content across various social media platforms, based on the results of the European Commission \"The rise of anti-semitism online during the pandemic: A study of French and German content,\" prepared in 2021 and covering the period January 2020 - March 2021. The study aim to understand the impact of the Covid-19 pandemic might have had on the proliferation of the online anti-semitism and which platforms are particulary prone to anti-semitic messaging in French and German. The results show that there is a significant difference between the engagement activity of anti-semitic posts between French language posts (9.5 millions) and German ones (2,148.8 millions). While most of the engagements with anti-semitic posts for both German and French language posts are found on Telegram, for French language posts Twitter has also a high engagement activity rate. "},
{"data": [{"data": [699139, 831791], "name": "Facebook"}, {"data": [56336, 3047374], "name": "Telegram"}, {"data": [896546, 68371], "name": "Twitter"}], "_data": [["Anti-semitic posts", "Facebook", "Telegram", "Twitter"], ["France", "699139", "56336", "896546"], ["Germany", "831791", "3047374", "68371"]], "labels": {"name": "Anti-semitic posts", "values": ["France", "Germany"]}, "metadata": {"link": "https://op.europa.eu/s/shTX", "type": "Problem", "unit": "Number of followers", "year": "2020-2021", "title": "Followers of Accounts with Anti-Semitic Posts", "topic": "Hate Speech", "method": "Data collection", "source": "European Commission. The Rise of Anti-Semitism Online During the Pandemic: A Study of French and German Content (Brussels: European Commission, April 2021)", "sub_topic": "Prelavalence of hate speech", "chart_number": "294", "geographical": "France, Germany"}, "description": "The chart presents the distribution of followers of accounts containing anti-semitic content across various social media platforms, based on the results of the European Commission \"The rise of anti-semitism online during the pandemic: A study of French and German content,\" prepared in 2021 and covering the period January 2020 - March 2021. The study aim to understand the impact of the Covid-19 pandemic might have had on the proliferation of the online anti-semitism and which platforms are particulary prone to anti-semitic messaging in French and German. The data shows that French and German anti-semitic accounts had a combined following of almost 5.6 million followers (including people following multiple channels across multiple platforms). When it comes to activity and engagement, French channels had a total of over 1.65 million followers, whilst German channels had more than double this number, with almost four million followers."},
{"data": [{"data": [160, 24, 13, 51, 25, 324, 43, 48, 149, 260, 185, 156, 215, 229, 254, 819, 431, 666, 2200, 336, 198, 1329], "name": "Total"}], "_data": [["Period", "Total"], ["July 2020", "160"], ["August 2020", "24"], ["September 2020", "13"], ["October 2020", "51"], ["November 2020", "25"], ["December 2020", "324"], ["January 2021", "43"], ["February 2021", "48"], ["March 2021", "149"], ["April 2021", "260"], ["May 2021", "185"], ["June 2021", "156"], ["July 2021", "215"], ["August 2021", "229"], ["September 2021", "254"], ["October 2021", "819"], ["November 2021", "431"], ["December 2021", "666"], ["January 2022", "2200"], ["February 2022", "336"], ["March 2022", "198"], ["April 2022", "1329"]], "labels": {"name": "Period", "values": ["July 2020", "August 2020", "September 2020", "October 2020", "November 2020", "December 2020", "January 2021", "February 2021", "March 2021", "April 2021", "May 2021", "June 2021", "July 2021", "August 2021", "September 2021", "October 2021", "November 2021", "December 2021", "January 2022", "February 2022", "March 2022", "April 2022"]}, "metadata": {"link": "https://digital-strategy.ec.europa.eu/en/library/reports-march-and-april-actions-fighting-covid-19-disinformation", "type": "Solution", "unit": "Number of accounts suspended", "year": "2020-2022", "title": "Accounts Suspended for Violations of Twitter COVID-19 Misleading Information Policy", "topic": "Disinformation", "method": "Self-reporting", "source": "Twitter. Twitter Report on COVID-19 Misinformation (May 2022)", "sub_topic": "Removal of disinformation", "chart_number": "295", "geographical": "Global"}, "description": "The chart presents the number of accounts suspended by Twitter, due to violations of its COVID-19 Misleading Information Policy, for the period July 2020 - April 2022. The report shows the efforts of Twitter to limit the spread of COVID-19 disinformation online and it is part of the European Commission's Code of Practice on Disinformation monitoring process."},
{"data": [{"data": [31, 23, 9, 7.1, 7.1, 6.8, 6.8, 2.9, 2.5, 1.3, 1, 0.8, 0.7], "name": "Share in total seizures"}], "_data": [["Category", "Share in total seizures"], ["Footware", "31"], ["Clothing, knitted or crocheted", "23"], ["Articles of leather, handbags", "9"], ["Toys and games", "7.1"], ["Perfumery and cosmetics", "7.1"], ["Watches", "6.8"], ["Electrical machinery and electronics", "6.8"], ["Vehicles' parts", "2.9"], ["Optical, photographic, medical apparatus", "2.5"], ["Jewellery", "1.3"], ["Pharmaceutical products", "1"], ["Machinery and mechanical appliances", "0.8"], ["Other", "0.7"]], "labels": {"name": "Category", "values": ["Footware", "Clothing, knitted or crocheted", "Articles of leather, handbags", "Toys and games", "Perfumery and cosmetics", "Watches", "Electrical machinery and electronics", "Vehicles' parts", "Optical, photographic, medical apparatus", "Jewellery", "Pharmaceutical products", "Machinery and mechanical appliances", "Other"]}, "metadata": {"link": "https://doi.org/10.1787/117e352b-en", "type": "Problem", "unit": "Per cent (%)", "year": "2017-2019", "title": "Categories of Dangerous Counterfeit Goods Imported into the European Union", "topic": "Illegal Products", "method": "Data collection", "source": "OECD/EUIPO. “Dangerous Fakes: Trade in Counterfeit Goods that Pose Health, Safety and Environmental Risks,” Illicit Trade (Paris: OECD Publishing, 2022)", "sub_topic": "Dangerous goods", "chart_number": "297", "geographical": "European Union"}, "description": "The chart presents the share of different types of dangerous goods destined to European Union member states, in the period 2017 - 2019, based on the OECD and the European Union Intellectual Property Office report \"Dangerous Fakes: Trade in Counterfeit Goods that Pose Health, Safety and Environmental Risks,\" published in March 2022.  Except for the data refering to first three categories of dangerous goods, which are mentioned in the report, the values for the other type of dangerous goods are not explicitely displayed in the source, therefore most of the value of data in this chart are approximate, determined with pixel count. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [32, 25, 22, 7, 5, 4.2, 2.2, 1.2, 0.9, 0.3, 0.2], "name": "Share in total seizures"}], "_data": [["Category", "Share in total seizures"], ["Perfumery and cosmetics", "32"], ["Clothing, knitted or crocheted", "25"], ["Toys and games", "22"], ["Vehicles' parts", "7"], ["Pharmaceutical products", "5"], ["Electrical machinery and electronics", "4.2"], ["Watches", "2.2"], ["Foodstuffs", "1.2"], ["Other made-up textile articles", "0.9"], ["Jewellery", "0.3"], ["Soap", "0.2"]], "labels": {"name": "Category", "values": ["Perfumery and cosmetics", "Clothing, knitted or crocheted", "Toys and games", "Vehicles' parts", "Pharmaceutical products", "Electrical machinery and electronics", "Watches", "Foodstuffs", "Other made-up textile articles", "Jewellery", "Soap"]}, "metadata": {"link": "https://doi.org/10.1787/117e352b-en", "type": "Problem", "unit": "Per cent (%)", "year": "2017-2019", "title": "Main Categories of Dangerous Products Subject to Counterfeiting", "topic": "Illegal Products", "method": "Data collection", "source": "OECD/EUIPO. “Dangerous Fakes: Trade in Counterfeit Goods that Pose Health, Safety and Environmental Risks,” Illicit Trade (Paris: OECD Publishing, 2022)", "sub_topic": "Dangerous goods", "chart_number": "298", "geographical": "Global"}, "description": "The chart presents the main dangerous product categories subject to counterfeiting, in the period 2017 - 2019, based on the OECD and the European Union Intellectual Property Office report \"Dangerous Fakes: Trade in Counterfeit Goods that Pose Health, Safety and Environmental Risks,\" published in March 2022. The report shows that the most frequently counterfeited product categories during 2017-2019 were perfumery and cosmetics (32% of global customs seizures), clothing (25%) and toys and games (22%). For the other categories of products displayed on the chart the value of data are approximate, determined with pixel count, as these values are not explicitely displayed in the source. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [35, 24, 24, 7, 5.8, 2.2, 1.7, 0.2, 0.1], "name": "Share in total seizures"}], "_data": [["Category", "Share in total seizures"], ["Perfumery and cosmetics", "35"], ["Clothing, knitted or crocheted", "24"], ["Toys and games", "24"], ["Vehicles' parts", "7"], ["Pharmaceutical products", "5.8"], ["Electrical machinery and electronics", "2.2"], ["Watches", "1.7"], ["Foodstuffs", "0.2"], ["Jewellery", "0.1"]], "labels": {"name": "Category", "values": ["Perfumery and cosmetics", "Clothing, knitted or crocheted", "Toys and games", "Vehicles' parts", "Pharmaceutical products", "Electrical machinery and electronics", "Watches", "Foodstuffs", "Jewellery"]}, "metadata": {"link": "https://doi.org/10.1787/117e352b-en", "type": "Problem", "unit": "Per cent (%)", "year": "2017-2019", "title": "Main Categories of Counterfeit Dangerous Goods Seized Destined to the European Union", "topic": "Illegal Products", "method": "Data collection", "source": "OECD/EUIPO. “Dangerous Fakes: Trade in Counterfeit Goods that Pose Health, Safety and Environmental Risks,” Illicit Trade (Paris: OECD Publishing, 2022)", "sub_topic": "Dangerous goods", "chart_number": "299", "geographical": "European Union"}, "description": "The chart presents the main categories of dangerous products destined to the European Union seized in the period 2017-2019, based on the OECD and the European Union Intellectual Property Office report \"Dangerous Fakes: Trade in Counterfeit Goods that Pose Health, Safety and Environmental Risks,\" published in March 2022. The report shows that among dangerous fakes imported to the EU, cosmetics were the most frequently seized products (35% of global seizures), followed by clothing (24%), toys and games (24%) and vehicles’ parts (7%). For some of the other categories of products displayed on the chart the value of data are approximate, determined with pixel count, as these values are not explicitely displayed in the source. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [46, 18, 18, 8, 7, 1.8, 1.1, 0.09, 0.01], "name": "Share in total seizures"}], "_data": [["Category", "Share in total seizures"], ["Perfumery and cosmetics", "46"], ["Clothing, knitted or crocheted", "18"], ["Toys and games", "18"], ["Vehicles' parts", "8"], ["Pharmaceutical products", "7"], ["Electrical machinery and electronics", "1.8"], ["Watches", "1.1"], ["Foodstuffs", "0.09"], ["Jewellery", "0.01"]], "labels": {"name": "Category", "values": ["Perfumery and cosmetics", "Clothing, knitted or crocheted", "Toys and games", "Vehicles' parts", "Pharmaceutical products", "Electrical machinery and electronics", "Watches", "Foodstuffs", "Jewellery"]}, "metadata": {"link": "https://doi.org/10.1787/117e352b-en", "type": "Problem", "unit": "Per cent (%)", "year": "2017-2019", "title": "Product Categories of Counterfeit Dangerous Goods Purchased Online", "topic": "Illegal Products", "method": "Data collection", "source": "OECD/EUIPO. “Dangerous Fakes: Trade in Counterfeit Goods that Pose Health, Safety and Environmental Risks,” Illicit Trade (Paris: OECD Publishing, 2022)", "sub_topic": "Dangerous goods", "chart_number": "300", "geographical": "European Union"}, "description": "The chart presents the main categories of dangerous products destined to the European Union purchased online in the period 2017-2019, based on the OECD and the European Union Intellectual Property Office report \"Dangerous Fakes: Trade in Counterfeit Goods that Pose Health, Safety and Environmental Risks,\" published in March 2022. The report shows that among dangerous counterfeit products purchased online, 46% were cosmetics items, followed by clothing (18%), toys and games (17%) and automotive spare parts (8%). For some of the other categories of products displayed on the chart the value of data are approximate, determined with pixel count, as these values are not explicitely mentioned in the source. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [53, 20, 9, 8, 5.5, 2.2, 2, 0.2, 0.1], "name": "Share in total seizures"}], "_data": [["Category", "Share in total seizures"], ["Toys and games", "53"], ["Perfumery and cosmetics", "20"], ["Clothing, knitted or crocheted", "9"], ["Vehicles' parts", "8"], ["Foodstuffs", "5.5"], ["Other made-up textile articles", "2.2"], ["Electrical machinery and electronics", "2"], ["Watches", "0.2"], ["Pharmaceutical products", "0.1"]], "labels": {"name": "Category", "values": ["Toys and games", "Perfumery and cosmetics", "Clothing, knitted or crocheted", "Vehicles' parts", "Foodstuffs", "Other made-up textile articles", "Electrical machinery and electronics", "Watches", "Pharmaceutical products"]}, "metadata": {"link": "https://doi.org/10.1787/117e352b-en", "type": "Problem", "unit": "Per cent (%)", "year": "2017-2019", "title": "Main Product Categories of Counterfeit Dangerous Goods Shipped by Vessel", "topic": "Illegal Products", "method": "Data collection", "source": "OECD/EUIPO. “Dangerous Fakes: Trade in Counterfeit Goods that Pose Health, Safety and Environmental Risks,” Illicit Trade (Paris: OECD Publishing, 2022)", "sub_topic": "Dangerous goods", "chart_number": "301", "geographical": "Global"}, "description": "The chart presents the main categories of dangerous products shipped by vessel in the period 2017-2019, based on the OECD and the European Union Intellectual Property Office report \"Dangerous Fakes: Trade in Counterfeit Goods that Pose Health, Safety and Environmental Risks,\" published in March 2022. The report shows that toys and games were the most frequently counterfeited products, equivalent to 53% of the global customs seizures of containerized dangerous goods. It was followed by cosmetics (20%), clothing (9%) and automotive spare parts (8%). For some of the other categories of products displayed on the chart the value of data are approximate, determined with pixel count, as these values are not explicitely mentioned in the source. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [42, 24, 24, 4, 3, 2, 0.5, 0.25, 0.25], "name": "Share in total seizures"}], "_data": [["Category", "Share in total seizures"], ["Perfumery and cosmetics", "42"], ["Clothing, knitted or crocheted", "24"], ["Toys and games", "24"], ["Vehicles' parts", "4"], ["Watches", "3"], ["Electrical machinery and electronics", "2"], ["Foodstuffs", "0.5"], ["Pharmaceutical products", "0.25"], ["Other", "0.25"]], "labels": {"name": "Category", "values": ["Perfumery and cosmetics", "Clothing, knitted or crocheted", "Toys and games", "Vehicles' parts", "Watches", "Electrical machinery and electronics", "Foodstuffs", "Pharmaceutical products", "Other"]}, "metadata": {"link": "https://doi.org/10.1787/117e352b-en", "type": "Problem", "unit": "Per cent (%)", "year": "2017-2019", "title": "Main Product Categories of Small Parcels of Counterfeit Dangerous Goods", "topic": "Illegal Products", "method": "Data collection", "source": "OECD/EUIPO. “Dangerous Fakes: Trade in Counterfeit Goods that Pose Health, Safety and Environmental Risks,” Illicit Trade (Paris: OECD Publishing, 2022)", "sub_topic": "Dangerous goods", "chart_number": "302", "geographical": "Global"}, "description": "The chart presents the main categories of dangerous counterfeit products found in small parcels in the period 2017-2019, based on the OECD and the European Union Intellectual Property Office report \"Dangerous Fakes: Trade in Counterfeit Goods that Pose Health, Safety and Environmental Risks,\" published in March 2022. The report shows that among these seizures, counterfeit cosmetics accounted for 42% of the global seizures of small parcels, followd by counterfeit clothing (24%) and counterfeit toys and games (24%). For some of the other categories of products displayed on the chart the value of data are approximate, determined with pixel count, as these values are not explicitely mentioned in the source. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [60, 11], "name": "Online purchase"}, {"data": [40, 89], "name": "Offline purchase"}], "_data": [["Category", "Online purchase", "Offline purchase"], ["Global customs seizures", "60", "40"], ["Global seized value", "11", "89"]], "labels": {"name": "Category", "values": ["Global customs seizures", "Global seized value"]}, "metadata": {"link": "https://doi.org/10.1787/117e352b-en", "type": "Problem", "unit": "Per cent (%)", "year": "2017-2019", "title": "Online and Offline Sales of Counterfeit Dangerous Goods in the European Union ", "topic": "Illegal Products", "method": "Data collection", "source": "OECD/EUIPO. “Dangerous Fakes: Trade in Counterfeit Goods that Pose Health, Safety and Environmental Risks,” Illicit Trade (Paris: OECD Publishing, 2022)", "sub_topic": "Dangerous goods", "chart_number": "303", "geographical": "Global"}, "description": "The chart presents the distribution of online and offline sales of dangerous counterfeit products in EU in the period 2017-2019, based on the OECD and the European Union Intellectual Property Office report \"Dangerous Fakes: Trade in Counterfeit Goods that Pose Health, Safety and Environmental Risks,\" published in March 2022. The report shows that while online sales represented 60% of global seizures of dangerous products destined to the EU, when it come to seized value, it amounts to only 11% of global seized value. European Union refers to EU28. The United Kingdom left the European Union on 31 January 2020."},
{"data": [{"data": [88.4, 88.4, 75], "name": "Value of trade of counterfeit dangerous products "}], "_data": [["Period", "Value of trade of counterfeit dangerous products "], ["2017", "88.4"], ["2018", "88.4"], ["2019", "75"]], "labels": {"name": "Period", "values": ["2017", "2018", "2019"]}, "metadata": {"link": "https://doi.org/10.1787/117e352b-en", "type": "Problem", "unit": "USD Billion ", "year": "2017-2019", "title": "Estimates of Global Trade of Counterfeit Dangerous Goods", "topic": "Illegal Products", "method": "Data collection", "source": "OECD/EUIPO. “Dangerous Fakes: Trade in Counterfeit Goods that Pose Health, Safety and Environmental Risks,” Illicit Trade (Paris: OECD Publishing, 2022)", "sub_topic": "Dangerous goods", "chart_number": "304", "geographical": "Global"}, "description": "The chart presents an estimation of the global trade of dangerous counterfeit products in the period 2017-2019, based on the OECD and the European Union Intellectual Property Office report \"Dangerous Fakes: Trade in Counterfeit Goods that Pose Health, Safety and Environmental Risks,\" published in March 2022. The report shows that the total volume of potential dangerous counterfeit products traded amounted to almost USD 75 billion in 2019, slightly lower than in 2017 and 2018, when it amounted to USD 88.4 billion. In addition, the report mentions also that the trade in dangerous counterfeit goods represented a third of global trade in counterfeit goods in 2019. "},
{"data": [{"data": [0.92, 3.52, 7.26, 11.21, 12.51, 15.38], "name": "Accounts suspected to be under the age of 13"}, {"data": [null, null, 2.41, 1.65, 1.73, 6.08], "name": "Fake accounts "}, {"data": [0.7, 0.97, 1.48, 2.09, 2.76, 2.65], "name": "Other"}], "_data": [["Period", "Accounts suspected to be under the age of 13", "Fake accounts ", "Other"], ["July-September 2020", "0.92", "", "0.7"], ["October-December 2020", "3.52", "", "0.97"], ["January-March 2021", "7.26", "2.41", "1.48"], ["April-June 2021", "11.21", "1.65", "2.09"], ["July-September 2021", "12.51", "1.73", "2.76"], ["October-December 2021", "15.38", "6.08", "2.65"]], "labels": {"name": "Period", "values": ["July-September 2020", "October-December 2020", "January-March 2021", "April-June 2021", "July-September 2021", "October-December 2021"]}, "metadata": {"link": "https://www.tiktok.com/transparency/en/community-guidelines-enforcement-2021-4/", "type": "Solution", "unit": "Number of accounts removed (Million)", "year": "2020-2021", "title": "Accounts Removed by TikTok for Policy Violations, by Type of Reason", "topic": "Illegal Content", "method": "Self-reporting", "source": "TikTok. Transparency Report: Community Guidelines Enforcement (June 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "308", "geographical": "Global"}, "description": "This chart shows the total number of accounts removed by Tiktok due to violation of its policy, from July 2020 until December 2021. The data shows that the total number of accounts removed in the last quarter of 2021 was more than five times higher than those removed in the same period of the previous year. Overall, the main reason of removal is the account's user age, with 63.8% account removed in the last quarter of 2021. "},
{"data": [{"data": [3.4, 4.9, 8.8, 17, 31, 28.4], "name": "Videos removed by automation"}, {"data": [39.7, 41.2, 53.1, 64.6, 60.4, 57.4], "name": "Other video removals"}], "_data": [["Period", "Videos removed by automation", "Other video removals"], ["July-September 2020", "3.4", "39.7"], ["October-December 2020", "4.9", "41.2"], ["January-March 2021", "8.8", "53.1"], ["April-June 2021", "17", "64.6"], ["July-September 2021", "31", "60.4"], ["October-December 2021", "28.4", "57.4"]], "labels": {"name": "Period", "values": ["July-September 2020", "October-December 2020", "January-March 2021", "April-June 2021", "July-September 2021", "October-December 2021"]}, "metadata": {"link": "https://www.tiktok.com/transparency/en/community-guidelines-enforcement-2021-4/", "type": "Solution", "unit": "Number of videos removed (million)", "year": "2020-2021", "title": "Videos Removed by TikTok for Policy Violations, by Type of Detection", "topic": "Illegal Content", "method": "Self-reporting", "source": "TikTok. Transparency Report: Community Guidelines Enforcement (June 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "309", "geographical": "Global"}, "description": "This chart shows the volume of videos removed by Tiktok for policy violation, from July 2020 until December 2021. The data shows that the volume of videos removed by TikTok in the last quarter of 2021 increased by 86% compared to the same period of the previous year. However, the total videos removed by TikTok represents about 1% of all videos uploaded on the social media platform."},
{"data": [{"data": [21.3, 19.7, 15.6, 14, 11.1, 10.9], "name": "Adult nudity and sexual activities"}, {"data": [4.9, 8.2, 8, 6.8, 5.3, 5.7], "name": "Harassment and bullying"}, {"data": [1.6, 2.5, 2.3, 2.2, 1.5, 1.5], "name": "Hateful behaviour"}, {"data": [15.8, 19.8, 21.1, 20.9, 16.6, 19.5], "name": "Illegal activities and regulated goods"}, {"data": [1.5, 3.1, 2, 0.8, 0.5, 0.6], "name": "Integrity and authenticity"}, {"data": [41, 31.2, 36.8, 41.3, 51, 45.1], "name": "Minor safety"}, {"data": [5.9, 6.5, 5.7, 5.3, 5.7, 7.4], "name": "Suicide, self-harm, and dangerous acts"}, {"data": [7.7, 8.6, 8, 7.7, 7.4, 8.5], "name": "Violent and graphic content"}, {"data": [0.2, 0.4, 0.5, 1, 0.9, 0.8], "name": "Violent extremism"}], "_data": [["Period", "Adult nudity and sexual activities", "Harassment and bullying", "Hateful behaviour", "Illegal activities and regulated goods", "Integrity and authenticity", "Minor safety", "Suicide, self-harm, and dangerous acts", "Violent and graphic content", "Violent extremism"], ["July-September 2020", "21.3", "4.9", "1.6", "15.8", "1.5", "41", "5.9", "7.7", "0.2"], ["October-December 2020", "19.7", "8.2", "2.5", "19.8", "3.1", "31.2", "6.5", "8.6", "0.4"], ["January-March 2021", "15.6", "8", "2.3", "21.1", "2", "36.8", "5.7", "8", "0.5"], ["April-June 2021", "14", "6.8", "2.2", "20.9", "0.8", "41.3", "5.3", "7.7", "1"], ["July-September 2021", "11.1", "5.3", "1.5", "16.6", "0.5", "51", "5.7", "7.4", "0.9"], ["October-December 2021", "10.9", "5.7", "1.5", "19.5", "0.6", "45.1", "7.4", "8.5", "0.8"]], "labels": {"name": "Period", "values": ["July-September 2020", "October-December 2020", "January-March 2021", "April-June 2021", "July-September 2021", "October-December 2021"]}, "metadata": {"link": "https://www.tiktok.com/transparency/en/community-guidelines-enforcement-2021-4/", "type": "Solution", "unit": "Per cent (%)", "year": "2020-2021", "title": "Videos Removed by TikTok for Policy Violations, by Type of Policy", "topic": "Illegal Content", "method": "Self-reporting", "source": "TikTok. Transparency Report: Community Guidelines Enforcement (June 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "310", "geographical": "Global"}, "description": "This chart shows the volume of videos removed by Tiktok for policy violation, from July 2020 until December 2021. A video may violate multiple policies and each violation is reflected. In certain rare circumstances, such as emergency situations or hardware outages, a removed video’s violation category may not be captured. These videos are not represented in the chart. The data shows that minors' safety is the main reason for video removal by TikTok (45% in the last quarter of 2021), follwed by illegal activities and regulated goods (19.5%) and adult nudity and sexual activities (11%)."},
{"data": [{"data": [28, 45, 135, 412, 2434, 1722], "name": "Government requests"}], "_data": [["Period", "Government requests"], ["Jan-June 2019", "28"], ["Jul-Dec 2019", "45"], ["Jan-June 2020", "135"], ["Jul-Dec 2020", "412"], ["Jan-June 2021", "2434"], ["Jul-Dec 2021", "1722"]], "labels": {"name": "Period", "values": ["Jan-June 2019", "Jul-Dec 2019", "Jan-June 2020", "Jul-Dec 2020", "Jan-June 2021", "Jul-Dec 2021"]}, "metadata": {"link": "https://www.tiktok.com/transparency/en/community-guidelines-enforcement-2021-4/", "type": "Solution", "unit": "Number of requests", "year": "2019-2021", "title": "Government Requests to TikTok to Remove or Restrict Content or Accounts", "topic": "Illegal Content", "method": "Self-reporting", "source": "TikTok. Transparency Report: Government Removal Requests (June 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "311", "geographical": "Global"}, "description": "The chart presents the volume of government removal or restriction requests received by TikTok and the platform type of response to these requests. All requests received from governments are reviewed and acted upon based on both TikTok Community Guidelines and Terms of Service and the applicable law. The reported content will be restricted if it is illegal in a country, but it is still in line with TikTok Community Guidelines standards. The platform rejects all the requests concerning content that is not illegal and does not infringe the TikTok Community Guidelines. The data shows that in the second half of 2021, the volume of goverment requests declined by 29% compared to the previous period, but it still remains four times higher than the similar period of 2020."},
{"data": [{"data": [78140, 11646, 49821], "name": "Total copyright take-down reports"}, {"data": [16662, 10667, 40469], "name": "Succesful copyright take-down reports"}], "_data": [["Period", "Total copyright take-down reports", "Succesful copyright take-down reports"], ["Jan-Apr 2021", "78140", "16662"], ["May-June 2021", "11646", "10667"], ["Jul-Dec 2021", "49821", "40469"]], "labels": {"name": "Period", "values": ["Jan-Apr 2021", "May-June 2021", "Jul-Dec 2021"]}, "metadata": {"link": "https://www.tiktok.com/transparency/en/community-guidelines-enforcement-2021-4/", "type": "Solution", "unit": "Number of requests", "year": "2021", "title": "Copyright Content Take-Down Notices Received by TikTok", "topic": "Illegal Content", "method": "Self-reporting", "source": "TikTok. Transparency Report: Intellectual Property Removal Requests (June 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "312", "geographical": "Global"}, "description": "The chart presents the number of copyright take-down requests received by TikTok and the number of succesfull copyrights take-downs in 2021. The TikTok Community Guidelines and Terms of Service prohibit content that infringes third party intellectual property. When valid take-down requests based on violations of copyright law and trademark law are received by the platform from the right holders, TikTok removes the alleged infringing content in a timely manner. There is a breakdown in data for the first half of 2021 and the data for the two periods are not directly comparable. In the period January - April 2021, data includes all copyright take-down notices received by the platform, while since May 2021, the data includes only valid copyright take-down notices. Valid copyright take-down notices are notices that include the statutorily defined elements in the DMCA, the EU Copyright Directive, and other similar law, that are required to report alleged copyright infringement. "},
{"data": [{"data": [4366, 1168, 6379], "name": "Total copyright take-down reports"}, {"data": [620, 958, 5372], "name": "Succesful copyright take-down reports"}], "_data": [["Period", "Total copyright take-down reports", "Succesful copyright take-down reports"], ["Jan-Apr 2021", "4366", "620"], ["May-June 2021", "1168", "958"], ["Jul-Dec 2021", "6379", "5372"]], "labels": {"name": "Period", "values": ["Jan-Apr 2021", "May-June 2021", "Jul-Dec 2021"]}, "metadata": {"link": "https://www.tiktok.com/transparency/en/community-guidelines-enforcement-2021-4/", "type": "Solution", "unit": "Number of requests", "year": "2021", "title": "Trademark Content Take-Down Notices Received by TikTok", "topic": "Illegal Content", "method": "Self-reporting", "source": "TikTok. Transparency Report: Intellectual Property Removal Requests (June 2022)", "sub_topic": "Prevalence of illegal content", "chart_number": "313", "geographical": "Global"}, "description": "The chart presents the number of trademark take-down requests received by TikTok and the number of succesfull take-downs in 2021. The TikTok Community Guidelines and Terms of Service prohibit content that infringes third party intellectual property. When valid take-down requests based on violations of copyright law and trademark law are received by the platform from the right holders, TikTok removes the alleged infringing content in a timely manner. There is a breakdown in data for the first half of 2021 and the data for the two periods are not directly comparable. In the period January - April 2021, data includes all trademark take-down notices received by the platform, while since May 2021, the data includes only valid trademark take-down notices. Valid trademark take-down notices are notices that provide sufficient information to assess if there has been trademark infringement according to applicable law."}]}