The Politics of Social Media Manipulation

Cover
Table of Contents
	1	The politics of social media manipulation
		Richard Rogers and Sabine Niederer
	2	Political news on Facebook during the 2019 Dutch elections
		Stijn Peeters and Richard Rogers
	3	Political news in search engines
		Exploring Google’s susceptibility to hyperpartisan sources during the Dutch elections
		Guillén Torres and Richard Rogers
	4	The circulation of political news on Twitter during the Dutch elections
		Sabine Niederer and Maarten Groen
	5	Dutch political Instagram
		Junk news, follower ecologies and artificial amplification
		Gabriele Colombo and Carlo De Gaetano
	6	Dutch junk news on Reddit and 4chan/pol
		Sal Hagen and Emilija Jokubauskaitė
	7	Fake news and the Dutch YouTube political debate space
		Marc Tuters
	8	Conclusions
		Mainstream under fire
		Richard Rogers and Sabine Niederer
	9	Epilogue
		After the tweet storm
		Richard Rogers and Sal Hagen
	References
	Index
List of figures and tables
	Figures
		Figure 1.1 Cartoon that ridicules the fake news taskforce, stating: ‘internet trolls are best countered by internet hobbits’
		Figure 1.2 ‘Detected and eliminated’ fake news, with a warning issued by NU.nl and Nieuwscheckers
		Figure 1.3 The birth of the fake news crisis, or ‘fake news’ outperforms ‘mainstream news’ on Facebook, in the run-up to the U.S. elections in 2016
		Figure 1.4 Facebook political ad library tool, results for Britain’s Future, 13 March 2019
		Figure 2.1 Engagement of mainstream (blue) and junk-like news (pink) articles found through provincial elections-related BuzzSumo queries, per week, between 18 February 2019 and 25 March 2019. Engagement scores have been normalized.
		Figure 2.2 Total Facebook engagement of fake versus mainstream news. Results from election-related queries on BuzzSumo, for the 20 most-engaged with articles between February and November 2016, per three-month period
		Figure 2.3 Per-query engagement of mainstream (blue) and junk (pink) articles found through provincial elections-related BuzzSumo queries, per week, between 18 February and 25 March 2019. Engagement scores have been normalized.
		Figure 2.4 Engagement of mainstream and junk-like articles found through EU elections-related queries on BuzzSumo, between 19 April 2019 and 23 May 2019. Engagement scores have been normalized.
		Figure 2.5 Per-query engagement of mainstream (blue) and junk (pink) articles found through EU parliamentary election-related BuzzSumo queries, per week, between 19 April 2019 and 23 May 2019. Engagement scores have been normalized.
		Figure 2.6 Engagement of mainstream, hyperpartisan, conspiracy and clickbait articles found for provincial elections-related queries on BuzzSumo, between 18 February 2019 and 25 March 2019. Engagement scores have been normalized. GeenStijl is considered
		Figure 2.7 Engagement of mainstream, tendentious, hyperpartisan, conspiracy and clickbait articles found for provincial elections-related queries on BuzzSumo, between 18 February 2019 and 25 March 2019. Engagement scores have been normalized. GeenStijl a
		Figure 2.8 Engagement of mainstream, tendentious, hyperpartisan, conspiracy and clickbait articles found for EU parliamentary elections-related queries on BuzzSumo, between 19 April 2019 and 23 May 2019. Engagement scores have been normalized. GeenStijl
		Figure 2.9 Engagement of mainstream, tendentious, hyperpartisan, conspiracy and clickbait articles found for EU parliamentary elections-related queries on BuzzSumo, between 19 April 2019 and 23 May 2019. Engagement scores have been normalized. GeenStijl
		Figure 2.10 Relative engagement of content categories across 4chan /pol/, Reddit, Twitter and Facebook. GeenStijl is considered ‘mainstream’ here while The Post Online is classified as ‘hyperpartisan’. 4chan and reddit data from 1 Dec 2015 until 1 June;
		Figure 2.11 Relative engagement of content categories across 4chan /pol/, Reddit, Twitter and Facebook. 4chan and reddit data from 1 Dec 2015 until 1 June; Twitter and Facebook data from 18 Feb 2019-25 Mar 2019 and 19 Apr 2019-23 May 2019
		Figure 3.1 Presence of junk news in Google.nl search engine results for political queries related to foreign affairs, 13-22 March 2019
		Figure 3.2 Presence of junk news in Google.nl search engine results for political queries related to polarizing topics, 13-22 March 2019
		Figure 3.3 Presence of junk news in Google.nl search engine results for political queries related to the environment, 13-22 March 2019
		Figure 3.4 Presence of junk news in Google.nl search engine results for political queries related to the economy, 13-22 March 2019
		Figure 3.5 Presence of junk news in Google.nl search engine results for political queries related to societal issues, 13-22 March 2019
		Figure 3.6 Presence of junk news in Google.nl search engine results for political queries related to future innovation, 13-22 March 2019
		Figure 3.7 Presence of junk news in Google.nl search engine results for political queries related to the environment, using language from the Facebook comment space of the political parties, 13-22 March 2019
		Figure 3.8 Presence of junk news in Google.nl search engine results for political queries related to foreign affairs, using language from the Facebook comment space of the political parties, 13-22 March 2019
		Figure 3.9 Presence of junk news in Google.nl search engine results for political queries related to polarizing topics, using language from the Facebook comment space  of the political parties, 13-22 March 2019
		Figure 3.10 Presence of junk news in Google.nl search engine results for political queries related to migration, using language from the Facebook comment space of the political parties, 13-22 March 2019
		Figure 3.11 Presence of junk news in Google.nl search engine results for political queries related to migration and European Union issues, 22-24 May 2019
		Figure 3.12 Presence of junk news in Google.nl search engine results for political queries related to climate and economic issues, 22-24 May 2019
		Figure 4.1 Political party leaders as trolling targets on Twitter during the 2017 Dutch general elections. Each dot represents one mention (by a user mentioning political leaders at least 100 times). Red represents an attack, and green represents a favou
		Figure 4.2 Engagement of mainstream (blue) and junk news (pink) articles during the Dutch Provincial election campaign (left) and the European Election campaign period (right)
		Figure 4.3 Engagement with mainstream news (blue) and junk news (pink) for the issue of Zwarte Piet (top left) and MH17 (top right) and during the Provincial elections, and the EU elections (bottom left and right)
		Figure 4.4 Tweet and user counts, top hashtags, and most-retweeted tweets during the Dutch provincial election period of 2019
		Figure 4.5 Gephi visualization of Zwarte Piet host-user network during the provincial elections campaign period, depicting only junk and tendentious hosts and the user accounts that circulate these sources
		Figure 4.6 Gephi visualization of MH17 host-user network during the provincial elections campaign period, depicting only junk and tendentious hosts and the user accounts that circulate these sources
		Figure 4.7 Gephi visualization of Utrecht shooting host-user network during the provincial elections campaign period, depicting only junk and tendentious hosts and the user accounts that circulate these sources
		Figure 4.8 Gephi visualization of PS2019 host-user network during the provincial elections campaign period, depicting only junk and tendentious hosts and the users that circulate these sources
		Figure 4.9 Gephi visualization of Party Leadership host-user network during the provincial elections campaign period, depicting only junk and tendentious hosts and the users that circulate these sources
		Alternate Figure 4.2 These line graphs visualize the engagement with mainstream news (blue) and junk news sources (pink) during the Dutch provincial election campaign (PS) and the European Election campaign period (EU), similar to Figure 4.2, but excludi
		Alternate Figure 4.3 These line graphs visualize the engagement with mainstream news (blue) and junk news sources (pink) for the issues of MH17 and Zwarte Piet during the provincial elections (PS), and the EU elections (EU), similar to Figure 4.3, but ex
		Figure 5.1 Diagram of the research protocol, showing the type of hashtags and accounts used for querying Instagram, and the tools used to collect, visualize and analyze the data
		Figure 5.2 Proportions of most liked content shared around the 2019 Dutch provincial elections, categorized as junk, satire, and not junk
		Figure 5.3 20 most-liked posts per hashtag shared around the 2019 Dutch provincial elections, sorted from right (most junk) to left (least junk)
		Figure 5.4 Examples of the posts flagged as hyperpartisan or satire
		Figure 5.5 Proportions of most-liked content shared around the 2019 European elections, categorized as junk and not junk
		Figure 5.6 20 most liked posts per hashtag shared around the 2019 European elections, sorted from right (most junk) to left (least junk) and grouped by type (elections, issues, political leaders, and parties). Posts flagged as hyperpartisan are coloured
		Figure 5.7 Follower ecologies in the Dutch political space, visualized as a co-follower network and manually annotated. In the network, accounts with higher amounts of shared followers (pink) are placed closer to each other.
		Figure 5.8 Degree of account fakeness according to report by the HypeAuditor tool. Accounts on the further right have more suspected ‘fake followers’ than accounts on the left side of the graphs.
		Figure 5.9 Visualization of the follower base of Mark Rutte’s personal and work accounts and Geert Wilders’ account, based on results from the HypeAuditor tool. Each follower base is segmented based on ‘audience type’ and geographical provenance. Popular
		Figure 6.1 The frontpage of Reddit (retrieved 11 June 2019)
		Figure 6.2 The index page of 4chan/pol/ (retrieved 11 June 2019)
		Figure 6.3 Total amount of posts and comments on one of the Dutch subreddits (see Appendix 6.1)
		Figure 6.4 Frequency of posts linking to Dutch junk news domains on Reddit
		Figure 6.5 Dutch versus non-Dutch subreddits in which Dutch junk news appears. Size of circle represents the overall number of posts in that subreddit within the timeframe, and colour represents the relative amount of posts with junk news.
		Figure 6.6 Dutch subreddits where Dutch junk news appear compared to the size of all Dutch subreddits. Size of circle represents the overall number of posts in that subreddit, and colour represents the relative amount of posts with junk news.
		Figures 6.7 and 6.8 All Dutch and non-Dutch subreddits where Dutch junk news appear compared to the size of all of Reddit. Size of circle represents the overall number of posts in that subreddit, and colour represents the relative amount of posts with ju
		Figure 6.9 Line graph of posts with Dutch country flags on 4chan/pol/
		Figure 6.10 Frequency of posts linking to Dutch junk news domains on 4chan/pol/
		Figure 6.11 Links to news (red) and non-news (blue) sources in posts in Dutch subreddits
		Figure 6.12 Links to news (red) and non-news (blue) sources in Dutch posts on 4chan/pol/
		Figure 6.13 Links to Dutch (orange) and non-Dutch (blue) news on Dutch subreddits
		Figure 6.14 Links to Dutch (orange) and non-Dutch (blue) news on Dutch subreddits
		Figure 6.15 Categories of news domains in posts on Dutch subreddits
		Figure 6.16 Categorized types of news from news sources posted 4chan/pol/
		Figure 6.17 Mean Reddit posts scores by Dutch junk news propagators (users who posted a link to a Dutch junk news domain at least twice) as reported by Pushshift API
		Figure 6.18 Subreddits where Dutch junk news domains are most often posted
		Figure 6.19 Most linked to Junk news domains on all of Reddit
		Figure 6.20 The top 1008 most posted YouTube videos in Dutch subreddits. Black labels denote deleted videos/channels. Ranked left to right, top to bottom
		Figure 6.21 The top 1008 most posted YouTube videos in Dutch subreddits, with video categories as an overlay. Black labels denote deleted videos/channels. Ranked left to right, top to bottom
		Figure 6.22 The top 1008 most posted YouTube videos in 4chan/pol/in posts with a Dutch country flag. Black labels denote deleted videos/channels. Ranked left to right, top to bottom
		Figure 6.23 The top 1008 most posted YouTube videos in 4chan/pol/in posts with a  Dutch country flag, with video categories as an overlay. Ranked left to right, top to bottom. Black labels denote deleted videos/channels
		Figure 7.1 Related channels on YouTube. Table where the top row displays the name of each Dutch political party and the columns below each of these are the media organizations associated with each party’s YouTube channel. 29 March 2019
		Figure 7.2 TheLvkrijger post: Translated into English: ‘He who is silent agrees! Don’t shut up anymore! This is your country! Claim it!’
		Figure 7.3 Related channels on YouTube. Panoramic graph of larger Dutch YouTube media sphere. This graph was produced two months apart on 29 March 2019 and again on 22 May 2019 with identical outcomes.
		Figure 7.4 Thumbnail diagram of the ‘fringe channels’’ top ten most popular videos
		Figure 7.5 Screenshot from the ‘About’ page on Cafe Weltschmertz’s YouTube channel which includes a sarcastic ‘trigger warning’ for viewers who might be angered by its frank approach to political debate, as well as crypto-normative espousal of ‘democrati
		Figure 7.6 Weighted word lists of the titles of all the videos from the political commentary channels
		Figure 7.7 Screenshot of a comment under the video of ‘Leukste YT Fragmenten’, referring to a ‘hopeless debate’ and the lack of consensus on the definition of ‘nepnieuws’
		Figure 7.8 Related channels on YouTube, 22 May 2019
	Tables
		Table 1.1 Overview of 2016 fake rallies planned and promoted, as listed in theUS indictment of 13 Russian nationals concerning foreign electioninterference
		Table 2.1 Top 10 sites per category (provincial elections), for all queries combined,sorted by overall engagement scores as reported by BuzzSumo
		Table 2.2 Top 10 sites per category (EU parliamentary elections), for all queriescombined, sorted by overall engagement scores as reported by BuzzSumo
		Table 2.3 Top 10 ‘hyperpartisan’ sites for both data sets (provincial and EUelections), sorted by overall engagement scores as reported by BuzzSumo
		Table 3.1 List of Dutch political parties under study
		Table 3.2 List of categories and political keywords used in the study
		Table 4.1 Query overview showing the election campaign period (Provincial, EUor both), the political or issue space and the query made resulting inTwitter data sets
		Table 5.1 Lists of hashtags pertaining to political leaders and politically chargeddiscussions used to demarcate the Dutch political space on Instagramaround the 2019 provincial elections
		Table 5.2 Lists of hashtags pertaining to political leaders and politically chargeddiscussions used to demarcate the Dutch political space on Instagramduring the months before the 2019 European elections
		Figure 6.17 Mean Reddit posts scores by Dutch junk news propagators (users whoposted a link to a Dutch junk news domain at least twice) as reportedby Pushshift API
		Table 6.2 Metrics of users who shared the Dutch junk news on Reddit
		Table 6.3 The most occurring YouTube channels from all YouTube links posted inthe Dutch Reddit and 4chan/pol/ samples. Data source: 4CAT, Pushshift,and YouTube API. Timeframe: 01-Dec-2015 to 01-Jun-2019
		Table 6.4 Compiled list of Dutch subreddits
		Table 6.5 Junk news categorization. Edited and enhanced list originating fromHoax-Wijzer. 23 March, 2019
		Table 6.6 Metrics for the proportions of news, Dutch news, Dutch junk news,and categories in posts on Dutch language subreddits, 01-Dec-2015 to01-Jun-2019
		Table 6.7 Metrics for the proportions of news, Dutch news, Dutch junk news,and categories in posts on 4chan/pol/ with a country flag from theNetherlands, 01-Dec-2015 to 01-Jun-2019
		Table 6.8 Most occurring URLs from posts containing links to RT.com and Sputnikby posts with a Dutch country flag on 4chan/pol/. Derived with 4CAT