Published Papers

The consistency principle: Crisis perceptions, partisanship and public support for democratic norms in comparative perspective
European Journal of Political Research. 2024. [Paper]
With Amanda Driscoll, Jay Krehbiel, and Michel J. Nelson

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.

Spatial modeling of dyadic geopolitical interactions between moving actors
Political Science Research and Methods. 2023. [Paper]
With Howard Liu and Bruce A. Desmarais

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

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

Political Communication in the Streaming-oriented Platform, Twitch
Under Review [Paper]

Until now, scholars of social media and politics have focused on text-oriented social media platforms, such as Facebook and Twitter, neglecting newer platforms focused on video and real-time chat. I investigate a video-oriented social media platform that has seen little attention from social scientists: Twitch. I study the patterns of political communication in the platform by focusing on the streaming chat of political “streamers”, a term for live broadcasters of the platform. In this paper, I aim to answer three questions on Twitch politics: 1) Who are political Twitch streamers? 2) What contents are covered in the political streams? 3) How do the political streamers and their audiences interact with each other? By using the supervised machine learning methods, I have identified 574 political streamers out of 59,272 total streamers, whose information is retrieved via the Twitch API. I have collected 33.6 million chat posts from political streamers’ live broadcasting and found 646,073 unique Twitch users who have posted at least one chat post. Using the wide corpus of text data, I conduct text analyses to observe what contents are covered in the political streams and network analyses to capture the interactions among political streamers and viewers of their stream.

Comparison of Credibility of News Shared in Four Different Platforms during Midterm Election 2022: Twitter, Facebook, Instagram, and Reddit
With Ozgur Can Seckin, Kaicheng Yang, and Fil Menzcer

Social media platforms have become primary sources for accessing and consuming political news, aligning with the ongoing digital transformation of the media landscape. While this transformation has facilitated easier access to information, concerns regarding the over-sharing of news from low-credibility sources and partisan-driven news sharing behaviors have emerged as significant issues for both the scientific community and policymakers. Despite various studies on this topic, there remains surprisingly little understanding of how users’ political news sharing behavior differs among different social media platforms. In this article, we compare the patterns of news sharing during a major political event, the United States 2022 midterm election, across three distinct social media platforms: Twitter, Meta (encompassing Facebook and Instagram), and Reddit. We leverage large-scale data collected during the election cycle. Our findings indicate differences in the credibility of news sources shared on each platform, both in terms of source credibility and partisanship. News sources shared on Reddit have higher credibility and are relatively left-leaning compared to those on Twitter and Meta. The study also reveals consistent patterns across all three platforms, indicating that right-leaning URLs tend to be associated with lower credibility, in line with existing literature. However, notable differences among the platforms emerge even when comparing URLs with similar partisan leanings. These findings underscore the importance of conducting multi-platform research on this topic, which can enhance our understanding of the overall news-sharing environment of social media.

The Political Influence of Non-Politicized Friends: How do social networks affect the spread of protest information in social media?
Dissertation Chapter

Work in progress

Twitching, Fast and Slow: Field Experiment in Political Stream
With Chloe Ahn, Drew Dimmery, and Kevin Munger

Quantifying the effects of time delay in illegal content takedown
With Bao Tran Truong, Natascha Just, Florian Saurwein, and Fil Menzcer