Research
Published Papers
The consistency principle: Crisis perceptions, partisanship and public support for democratic norms in comparative perspective
European Journal of Political Research. 2025. [Paper]
With Amanda Driscoll, Jay Krehbiel, and Michel J. Nelson
Abstract
A growing body of research theorizes that partisanship can undermine democracy as citizens prioritize their political interests over abstract norms and values. We argue that crises might counteract intense partisanship by giving citizens clarity on the threats posed by rule of law violations. Examining the differential application of a law – a breach of democratic norms – we draw on an experiment embedded in representative surveys of Germany, the United States, Hungary and Poland to examine citizens’ sense of appropriate punishment for elites’ violation of a municipal mask-wearing ordinance. We find evidence of partisan bias in citizens’ willingness to support punishment in all four countries. But, in the two consolidated democracies, we find that concern about the Covid-19 crisis diminishes partisan biases in punishment preferences: citizens who are most concerned about the crisis also model the most consistency in their willingness to hold copartisans into account.Understanding political communication and political communicators on Twitch
PLOS ONE. 2024. [Paper]
Abstract
As new technologies rapidly reshape patterns of political communication, platforms like Twitch are transforming how people consume political information. This entertainment-oriented live streaming platform allows us to observe the impact of technologies such as "live-streaming" and "streaming-chat" on political communication. Despite its entertainment focus, Twitch hosts a variety of political actors, including politicians and pundits. This study explores Twitch politics by addressing three main questions: 1) Who are the political Twitch streamers? 2) What content is covered in political streams? 3) How do audiences of political streams interact with each other? To identify political streamers, I leveraged the Twitch API and supervised machine-learning techniques, identifying 574 political streamers. I used topic modeling to analyze the content of political streams, revealing seven broad categories of political topics and a unique pattern of communication involving context-specific "emotes." Additionally, I created user-reference networks to examine interaction patterns, finding that a small number of users dominate the communication network. This research contributes to our understanding of how new social media technologies influence political communication, particularly among younger audiences.Spatial modeling of dyadic geopolitical interactions between moving actors
Political Science Research and Methods. 2023. [Paper]
With Howard Liu and Bruce A. Desmarais
Abstract
Political actors often interact spatially, and move around. However, with a few exceptions, existing political research has analyzed spatial dependence among actors with fixed geographic locations. Focusing on fixated geographic units prevents us from probing dependencies in spatial interaction between spatially dynamic actors, which are common in some areas of political science, such as sub-national conflict studies. In this note, we propose a method to account for spatial dependence in dyadic interactions between moving actors. Our method uses the spatiotemporal histories of dyadic interactions to project locations of future interactions—projected actor locations (PALs). PALs can, in turn, be used to model the likelihood of future dyadic interactions. In a replication and extension of a recent study of subnational conflict, we find that using PALs improves the predictive performance of the model and indicates that there is a clear relationship between actors’ past conflict locations and the likelihood of future conflicts.The effects of an informational intervention on attention to anti-vaccination content on YouTube
Proceedings of the International AAAI Conference on Web and Social Media. 2020. [Paper]
With Omer F. Yalcin, Samuel E. Bestvater, Kevin Munger, Burt L. Monroe, and Bruce A. Desmarais
Abstract
The spread of misinformation related to health, especially vaccination, is a potential contributor to myriad public health problems. This misinformation is frequently spread through social media. Recently, social media companies have intervened in the dissemination of misinformation regarding vaccinations. In the current study we focus on YouTube. Recognizing the extent of the problem, YouTube implemented an informational modification that affected many videos related to vaccination beginning in February 2019. We collect original data and analyze the effects of this intervention on video viewership. We find that this informational intervention reduced traffic to the affected videos, both overall, and in comparison to a carefully-matched set of control videos that did not receive the informational modification.Working Papers
Audited takedown delays across social media reveal failure to reduce exposure to illegal content
With Bao Tran Truong, Erfan Samieyan Sahneh, Gianluca Nogara, Enrico Verdolotti, Florian Saurwein-Scherer, Natascha Just, Luca Luceri, Silvia Giordano, and Filippo Menczer
Preprint arXiv:2502.08841 (2025) [Paper]
Abstract
Social media platforms face legal and regulatory pressures to moderate illegal content through takedown procedures. However, the effectiveness of content moderation varies widely across platforms due to differences in takedown deadlines imposed by various regulations. This study models the relationship between the timeliness of content removal and the persistence of illegal material on social media. By simulating illegal content diffusion using empirical data from sources like the DSA Transparency Database and Facebook NetzDG reports, we demonstrate that while rapid takedown (within hours) significantly reduces illegal content prevalence and exposure, longer delays (beyond 23 days) render moderation efforts futile. Our findings stress the need for regulatory frameworks with enforceable, short deadlines, such as those outlined in German law, to ensure meaningful content removal. These insights provide critical recommendations for policymakers aiming to enhance online safety and improve moderation strategies.LLMs can infer political alignment from online conversations
With Byunghwee Lee, Yong-Yeol Ahn, Filippo Menczer, Jisun An, and Haewoon Kwak
Abstract
Correlational structure in our trait (e.g., identities, cultures, and political attitudes) means that seemingly innocuous public social signals, such as following a band or using a specific slang, can reveal private traits. This possibility, especially when combined with massive, public social data and advanced computational methods, poses a fundamental privacy risk. Given our increasing data exposure online and the rapid advancement of AI are increasing the misuse potential of such risk, it is therefore critical to understand capacity of large language models (LLMs) to exploit it. Here, using online discussions on Debate.org and Reddit, we show that LLMs can reliably infer hidden political alignment, significantly outperforming traditional machine learning models. Prediction accuracy further improves as we aggregate multiple text-level inferences into a user-level prediction, and as we use more politics-adjacent domains. We demonstrate that LLMs leverage the words that can be highly predictive of political alignment while not being explicitly political. Our findings underscore the capacity and risks of LLMs for exploiting socio-cultural correlates.Pace and scale drive toxicity in live political discourse
With Hayden Arnold and Taegyoon Kim
Abstract
Livestreaming platforms have become consequential arenas for political communication, yet scholars have limited understanding of how toxic expression emerges in their fast-moving and highly interactive chat environments. This study examines how structural features, political content, and moderation jointly shape toxicity in political livestreams on Twitch. Drawing on a novel dataset of approximately 116 million public chat messages from 16,432 broadcast sessions across 453 English-language political channels, we analyze toxicity dynamics using a fine-tuned language model and dynamic panel models that account for temporal dependence and unobserved heterogeneity. We identify two countervailing structural forces that play a central role in shaping toxic expression. Faster-paced chats are substantially more toxic, consistent with reduced visibility and accountability in highly ephemeral conversations. In contrast, larger audiences are associated with lower toxicity, suggesting that heightened perceived scrutiny and stronger normative salience can discipline behavior even in large-scale crowds. Political content is also positively associated with toxicity, while visible moderation activity shows little evidence of a preventive effect, consistent with the limits of reactive governance under real-time constraints. Together, these findings demonstrate that toxicity in political livestreaming is shaped by the interaction of structural pace, audience scale, and political salience, and they underscore how the distinctive communicative architecture of livestreaming fundamentally alters the conditions under which hostile political discourse emerges. The study contributes new empirical evidence on real-time political communication and highlights important implications for platform governance and the design of healthier digital public spheres.The political influence of non-politicized friends: How do social networks affect the spread of protest information in social media?
Under review
Abstract
How do social networks influence the spread of protest information on social media? This article argues that the political characteristics of accounts sharing protest information affect how that information is interpreted and spread by other Twitter users. Specifically, I suggest that whether Twitter accounts are perceived as overtly political or nonpolitical can shape how users respond to signals about political protests. I hypothesize that nonpolitical accounts may exert more influence in spreading protest messages than political accounts, as they are seen as less biased or more trustworthy. To test this theory, I conducted an online experiment using vignettes that simulate the Twitter environment. Participants were exposed to protest-related Tweets and were asked whether they would retweet or like them, with some accounts presenting political traits in their profiles and others appearing nonpolitical. Contrary to my expectations, the results did not reveal a statistically significant difference in participants’ responses between political and nonpolitical profiles. However, the study revealed unexpected patterns, including the role of education in shaping retweet behavior differently across political groups and the influence of context-specific factors, such as protest types and images, on user engagement. These findings suggest that individual characteristics and content features may interact in complex ways, warranting further exploration.Diversity and quality trade-offs in political news sources on different social media platforms
With Ozgur Can Seckin, Filippo Menczer, and Kai-Cheng Yang
Abstract
Healthy online platforms should help users make well-informed decisions by providing them access to both diverse and high-quality information. To assess how effectively different social media platforms provide such information, we conduct a comprehensive analysis of political news-sharing activities during the 2022 U.S. midterm elections across five platforms: Twitter, Facebook, Instagram, Reddit, and 4chan. We classify news sources based on their credibility, partisan leaning, audience demographics, and geographic concentration to examine information quality and multiple dimensions of diversity. Our results reveal two key trade-offs: no platform successfully provides both high-quality and politically diverse news sources, and no platform achieves high diversity across all dimensions. These trade-offs indicate that platforms offering diversity along certain dimensions may inadvertently create echo chambers along others and that interventions focused on improving either information quality or diversity alone may have unintended consequences. Our findings illuminate the state of the online information environment during a major political event, demonstrate the value of multi-platform research, and emphasize the need for carefully designed intervention strategies that account for the complex interplay between information quality and diversity.The rise of Bluesky
With Ozgur Can Seckin, Filipi Nascimento Silva, Bao Tran Truong, Fan Huang, Nick Liu, Alessandro Flammini, and Filippo Menczer
Preprint arXiv:2504.12902 (2025) [Paper]
Abstract
This study investigates the rapid growth and evolving network structure of Bluesky from August 2023 to February 2025. Through multiple waves of user migrations, the platform has reached a stable, persistently active user base. The growth process has given rise to a dense follower network with clustering and hub features that favor viral information diffusion. These developments highlight engagement and structural similarities between Bluesky and established platforms.Work in progress
Twitching, fast and slow: Field experiment in political stream
With Chloe Ahn, Drew Dimmery, and Kevin Munger
Swift in the end zone: Identity threat in a male-dominated space
With Riley Anderson, Byunghwee Lee, and Hyun Joon Park
Abstract
This study examines the experience of psychological threat in response to Taylor Swift’s visibility in National Football League (NFL) media coverage. As many of her fans—often women and LGBTQ+ individuals—began to take an interest in football, the traditionally male-dominated space of the NFL may have perceived her presence as threatening. In particular, this dynamic may be more threatening to conservatives, who tend to place a higher value on traditional norms. To investigate this phenomenon, we conducted two complementary studies. In Study 1 (N = 398), we carried out a pre-registered experiment to test whether Taylor Swift’s association with the NFL elicited greater psychological threat, especially among conservatives. In Study 2 (N = 10,813), we analyzed comments from the r/NFL subreddit that referenced Taylor Swift, examining how Reddit users reacted to her presence in the NFL and whether sentiments toward her were linked to political orientation. Taken together, these studies shed light on how cultural figures entering male-dominated domains can provoke identity-based threat and elicit politically patterned responses.Sexism, support for violence, and democratic support: Evidence from South Korea
With Boyoon Lee and Yoonseok Lee