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1. Cross-Partisan Discussions on YouTube: Conservatives Talk to Liberals but Liberals Don't Talk to Conservatives

   Wu, Siqi; Resnick, Paul (May 2021, Proceedings of the Fifteenth International AAAI Conference on Web and Social Media)

   Budak, Ceren; Cha, Meeyoung; Quercia, Daniele; Xie, Lexing (Ed.)

   We present the first large-scale measurement study of cross-partisan discussions between liberals and conservatives on YouTube, based on a dataset of 274,241 political videos from 973 channels of US partisan media and 134M comments from 9.3M users over eight months in 2020. Contrary to a simple narrative of echo chambers, we find a surprising amount of cross-talk: most users with at least 10 comments posted at least once on both left-leaning and right-leaning YouTube channels. Cross-talk, however, was not symmetric. Based on the user leaning predicted by a hierarchical attention model, we find that conservatives were much more likely to comment on left-leaning videos than liberals on right-leaning videos. Secondly, YouTube's comment sorting algorithm made cross-partisan comments modestly less visible; for example, comments from conservatives made up 26.3% of all comments on left-leaning videos but just over 20% of the comments were in the top 20 positions. Lastly, using Perspective API's toxicity score as a measure of quality, we find that conservatives were not significantly more toxic than liberals when users directly commented on the content of videos. However, when users replied to comments from other users, we find that cross-partisan replies were more toxic than co-partisan replies on both left-leaning and right-leaning videos, with cross-partisan replies being especially toxic on the replier's home turf. 

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2. Quick, Community-Specific Learning: How Distinctive Toxicity Norms Are Maintained in Political Subreddits.

   Rajadesingan, Ashwin; Resnick, Paul; Budak, Ceren (May 2020, Proceedings of the International AAAI Conference on Web and Social Media)

   null (Ed.)

   Online communities about similar topics may maintain very different norms of interaction. Past research identifies many processes that contribute to maintaining stable norms, including self-selection, pre-entry learning, post-entry learning, and retention. We analyzed political subreddits that had distinctive, stable levels of toxic comments on Reddit, in order to identify the relative contribution of these four processes. Surprisingly, we find that the largest source of norm stability is pre-entry learning. That is, newcomers' first comments in these distinctive subreddits differ from those same people's prior behavior in other subreddits. Through this adjustment, they nearly match the toxicity level of the subreddit they are joining. We also show that behavior adjustments are community-specific and not broadly transformative. That is, people continue to post toxic comments at their previous rates in other political subreddits. Thus, we conclude that in political subreddits, compatible newcomers are neither born nor made– they make local adjustments on their own. 

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3. 'Walking Into a Fire Hoping You Don't Catch': Strategies and Designs to Facilitate Cross-Partisan Online Discussions

   https://doi.org/10.1145/3479537  

   rajadesingan, ashwin; Duran, Carolyn; Resnick, Paul; Budak, Ceren (October 2021, Proceedings of the ACM on Human-Computer Interaction)

   While cross-partisan conversations are central to a vibrant deliberative democracy, these conversations are hard to have, especially amidst unprecedented levels of partisan animosity we observe today. We report on a qualitative study of 17 US residents who engage with outpartisans on Reddit to understand what they look for in these interactions, and the strategies they adopt. We find that users have multiple, sometimes contradictory expectations of these conversations, ranging from deliberative discussions to entertainment and banter. In aiming to foster 'good' cross-partisan discussions, users make strategic choices on which subreddits to participate in, who to engage with and how to talk to outpartisans, often establishing common ground, complimenting, and remaining dispassionate in their interactions. Further, contrary to offline settings where knowing more about outpartisan interlocutors help manage disagreements, on Reddit, users look to actively learn as little as possible about them for fear that such information may bias their interactions. However, through design probes, we find that users are actually open to knowing certain kinds of information about their interlocutors, such as non-political subreddits that they both participate in, and to having that information made visible to their interlocutors. However, making other information visible, such as the other subreddits that they participate in or their past comments, though potentially humanizing, raises concerns around privacy and misuse of that information for personal attacks especially among women and minority groups. Finally, we identify important challenges and opportunities in designing to improve online cross-partisan interactions in today's hyper-polarized environment. 

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4. The Interaction between Political Typology and Filter Bubbles in News Recommendation Algorithms

   https://doi.org/10.1145/3442381.3450113  

   Liu, Ping; Shivaram, Karthik; Culotta, Aron; Shapiro, Matthew A.; Bilgic, Mustafa (April 2021, WWW '21: Proceedings of the Web Conference 2021)

   null (Ed.)

   Algorithmic personalization of news and social media content aims to improve user experience; however, there is evidence that this filtering can have the unintended side effect of creating homogeneous "filter bubbles," in which users are over-exposed to ideas that conform with their preexisting perceptions and beliefs. In this paper, we investigate this phenomenon in the context of political news recommendation algorithms, which have important implications for civil discourse. We first collect and curate a collection of over 900K news articles from 41 sources annotated by topic and partisan lean. We then conduct simulation studies to investigate how different algorithmic strategies affect filter bubble formation. Drawing on Pew studies of political typologies, we identify heterogeneous effects based on the user's pre-existing preferences. For example, we find that i) users with more extreme preferences are shown less diverse content but have higher click-through rates than users with less extreme preferences, ii) content-based and collaborative-filtering recommenders result in markedly different filter bubbles, and iii) when users have divergent views on different topics, recommenders tend to have a homogenization effect. 

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5. Polarizing Political Polls: How Visualization Design Choices Can Shape Public Opinion and Increase Political Polarization

   https://doi.org/10.1109/TVCG.2023.3326512  

   Holder, Eli; Bearfield, Cindy Xiong (January 2023, IEEE Transactions on Visualization and Computer Graphics)

   While we typically focus on data visualization as a tool for facilitating cognitive tasks (e.g. learning facts, making decisions), we know relatively little about their second-order impacts on our opinions, attitudes, and values. For example, could design or framing choices interact with viewers' social cognitive biases in ways that promote political polarization? When reporting on U.S. attitudes toward public policies, it is popular to highlight the gap between Democrats and Republicans (e.g. with blue vs red connected dot plots). But these charts may encourage social-normative conformity, influencing viewers' attitudes to match the divided opinions shown in the visualization. We conducted three experiments examining visualization framing in the context of social conformity and polarization. Crowdworkers viewed charts showing simulated polling results for public policy proposals. We varied framing (aggregating data as non-partisan “All US Adults,” or partisan “Democrat” / “Republican”) and the visualized groups' support levels. Participants then reported their own support for each policy. We found that participants' attitudes biased significantly toward the group attitudes shown in the stimuli and this can increase inter-party attitude divergence. These results demonstrate that data visualizations can induce social conformity and accelerate political polarization. Choosing to visualize partisan divisions can divide us further. 

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