Political Bias and Factualness in News Sharing Across more then 100, 000 Online Communities

As civil discourse increasingly takes place online, misinformation and the polarization of news shared in online communities have become ever more relevant concerns with real world harms across our society. Studying online news sharing at scale is challenging due to the massive volume of content which is shared by millions of users across thousands of communities. Therefore, existing research has largely focused on specific communities or specific interventions, such as bans. However, understanding the prevalence and spread of misinformation and polarization more broadly, across thousands of online communities, is critical for the development of governance strategies, interventions, and community design. Here, we conduct the largest study of news sharing on reddit to date, analyzing more than 550 million links spanning 4 years. We use non-partisan news source ratings from Media Bias/Fact Check to annotate links to news sources with their political bias and factualness. We find that, compared to leftleaning communities, right-leaning communities have 105% more variance in the political bias of their news sources, and more links to relatively-more biased sources, on average. We observe that reddit users’ voting and re-sharing behaviors generally decrease the visibility of extremely biased and low factual content, which receives 20% fewer upvotes and 30% fewer exposures from crossposts than more neutral or more factual content. This suggests that reddit is more resilient to low factual content than Twitter. We show that extremely biased and low factual content is very concentrated, with 99% of such content being shared in only 0.5% of communities, giving credence to the recent strategy of community-wide bans and quarantines.

[1]  Paul Resnick,et al.  Quick, Community-Specific Learning: How Distinctive Toxicity Norms Are Maintained in Political Subreddits , 2020, ICWSM.

[2]  Emilio Ferrara,et al.  Contagion dynamics of extremist propaganda in social networks , 2017, Inf. Sci..

[3]  James Zou,et al.  Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings , 2019, NAACL.

[4]  Ralf Krestel,et al.  Top Comment or Flop Comment? Predicting and Explaining User Engagement in Online News Discussions , 2020, ICWSM.

[5]  A. Bruckman,et al.  Human-Machine Collaboration for Content Regulation: The Case of Reddit Automoderator , 2019, ACM Trans. Comput. Hum. Interact..

[6]  Christo Wilson,et al.  Bias Misperceived: The Role of Partisanship and Misinformation in YouTube Comment Moderation , 2019, ICWSM.

[7]  Kimberley R. Allison,et al.  Communal Quirks and Circlejerks: A Taxonomy of Processes Contributing to Insularity in Online Communities , 2020, ICWSM.

[8]  Tim Weninger,et al.  Consumers and Curators: Browsing and Voting Patterns on Reddit , 2017, IEEE Transactions on Computational Social Systems.

[9]  Pamela Bilo Thomas,et al.  Behavior Change in Response to Subreddit Bans and External Events , 2021, ArXiv.

[10]  Jean-Charles Delvenne,et al.  The anatomy of Reddit: An overview of academic research , 2017, Dynamics On and Of Complex Networks III.

[11]  Gireeja Ranade,et al.  YouTube Chatter: Understanding Online Comments Discourse on Misinformative and Political YouTube Videos , 2019, ArXiv.

[12]  Preslav Nakov,et al.  Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information , 2019, INTERSPEECH.

[13]  Jinyoung Han,et al.  Rumor Propagation is Amplified by Echo Chambers in Social Media , 2020, Scientific Reports.

[14]  Casey Fiesler,et al.  “Participant” Perceptions of Twitter Research Ethics , 2018 .

[15]  Ceren Budak,et al.  Higher Ground? How Groundtruth Labeling Impacts Our Understanding of Fake News about the 2016 U.S. Presidential Nominees , 2020, ICWSM.

[16]  Deniz Üstebay,et al.  US Presidential Election: What Engaged People on Facebook , 2017, ICWSM.

[17]  Jabra Zarka,et al.  Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter , 2020, Cureus.

[18]  Amy Bruckman,et al.  Does Transparency in Moderation Really Matter? , 2019, Proc. ACM Hum. Comput. Interact..

[19]  Preslav Nakov,et al.  We Can Detect Your Bias: Predicting the Political Ideology of News Articles , 2020, EMNLP.

[20]  Thomas J. Main,et al.  The Rise of the Alt-Right , 2018 .

[21]  Tal August,et al.  The Effect of Moderation on Online Mental Health Conversations , 2020, ICWSM.

[22]  Svitlana Volkova,et al.  Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter , 2017, ACL.

[23]  Darren L. Linvill,et al.  Troll Factories: Manufacturing Specialized Disinformation on Twitter , 2020 .

[24]  Tanushree Mitra,et al.  Characterizing the Social Media News Sphere through User Co-Sharing Practices , 2020, ICWSM.

[25]  Michael S. Bernstein,et al.  PolicyKit: Building Governance in Online Communities , 2020, UIST.

[26]  Eric Gilbert,et al.  The Internet's Hidden Rules , 2018, Proceedings of the ACM on Human-Computer Interaction.

[27]  Bryan C. Semaan,et al.  Moderation Practices as Emotional Labor in Sustaining Online Communities: The Case of AAPI Identity Work on Reddit , 2019, CHI.

[28]  Derek Ruths,et al.  The Aftermath of Disbanding an Online Hateful Community , 2018, ArXiv.

[29]  Hernán A. Makse,et al.  CUNY Academic Works , 2022 .

[30]  Jisun An,et al.  Empirical Evaluation of Three Common Assumptions in Building Political Media Bias Datasets , 2020, ICWSM.

[31]  Dragomir R. Radev,et al.  Rumor has it: Identifying Misinformation in Microblogs , 2011, EMNLP.

[32]  Jeffrey A. Gottfried,et al.  News use across social media platforms 2016 , 2016 .

[33]  Jeremy Blackburn,et al.  The Pushshift Reddit Dataset , 2020, ICWSM.

[34]  J. Nathan Matias,et al.  The Civic Labor of Volunteer Moderators Online , 2019, Social Media + Society.

[35]  D. Lazer,et al.  Fake news on Twitter during the 2016 U.S. presidential election , 2019, Science.

[36]  Tim Weninger,et al.  Propagation From Deceptive News Sources Who Shares, How Much, How Evenly, and How Quickly? , 2018, IEEE Transactions on Computational Social Systems.

[37]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[38]  Preslav Nakov,et al.  Predicting the Topical Stance and Political Leaning of Media using Tweets , 2020, ACL.

[39]  Eshwar Chandrasekharan,et al.  Crossmod: A Cross-Community Learning-based System to Assist Reddit Moderators , 2019, Proc. ACM Hum. Comput. Interact..

[40]  Jacob Eisenstein,et al.  You Can't Stay Here , 2017, Proc. ACM Hum. Comput. Interact..

[41]  Alice E. Marwick,et al.  Media Manipulation and Disinformation Online , 2017 .

[42]  Duncan Watts,et al.  Evaluating the fake news problem at the scale of the information ecosystem , 2019, Science Advances.

[43]  Srayan Datta,et al.  Extracting Inter-community Conflicts in Reddit , 2018, ICWSM.

[44]  Raquel Recuero,et al.  Hyperpartisanship, Disinformation and Political Conversations on Twitter: The Brazilian Presidential Election of 2018 , 2020, ICWSM.

[45]  Ashton Anderson,et al.  Generalists and Specialists: Using Community Embeddings to Quantify Activity Diversity in Online Platforms , 2019, WWW.

[46]  Walid Magdy,et al.  Trump vs. Hillary: What Went Viral During the 2016 US Presidential Election , 2017, SocInfo.

[47]  Casey Fiesler,et al.  Reddit Rules! Characterizing an Ecosystem of Governance , 2018, ICWSM.

[48]  Kate Starbird,et al.  Examining the Alternative Media Ecosystem Through the Production of Alternative Narratives of Mass Shooting Events on Twitter , 2017, ICWSM.

[49]  Md Mahbub Hossain,et al.  Impact of Rumors and Misinformation on COVID-19 in Social Media , 2020, Journal of preventive medicine and public health = Yebang Uihakhoe chi.

[50]  Gianluca Stringhini,et al.  Do Platform Migrations Compromise Content Moderation? Evidence from r/The_Donald and r/Incels , 2020, Proc. ACM Hum. Comput. Interact..

[51]  Christo Wilson,et al.  Reasoning about Political Bias in Content Moderation , 2020, AAAI.

[52]  Sinan Aral,et al.  The spread of true and false news online , 2018, Science.

[53]  Sibel Adali,et al.  Different Spirals of Sameness: A Study of Content Sharing in Mainstream and Alternative Media , 2019, ICWSM.

[54]  Bryan C. Semaan,et al.  Social Media Is Polarized, Social Media Is Polarized: Towards a New Design Agenda for Mitigating Polarization , 2018, Conference on Designing Interactive Systems.

[55]  J. N. Matias Preventing harassment and increasing group participation through social norms in 2,190 online science discussions , 2019, Proceedings of the National Academy of Sciences.

[56]  A. Bruckman,et al.  Quarantined! Examining the Effects of a Community-Wide Moderation Intervention on Reddit , 2020, ACM Trans. Comput. Hum. Interact..