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Intermedia agenda setting (IAS) theory suggests that different news sources can influence each other's agenda. While this theory has been well-established in existing literature, whether it still holds in today's high-choice media environment, which includes news producers of different credibility and ideology dispositions, is an open question. Through two case studies--the 2016 and 2020 U.S. presidential elections--we show that media are still largely aligned, especially in broad topics they choose to cover, and that the level of alignment along the credibility dimension is comparable to that along the ideology dimension. Furthermore, we find that the coverage of the Republican candidate is better aligned across different media types than that of the Democratic candidate, and that media divergence has increased along both dimensions from 2016 to 2020. Finally, we demonstrate that high-credibility media still plays a dominant role in the IAS process, yet with a cautious warning of its declining IAS power for the Democratic candidate over the course of four years. 

The past decade in the US has been one of the most politically polarizing in recent memory. Ordinary Democrats and Republicans fundamentally dislike and distrust each other, even when they agree on policy issues. This increase in hostility towards opposing party supporters, commonly called affective polarization, has important ramifications that threaten democracy. Political science research suggests that at least part of this polarization stems from Democrats' misperceptions about Republicans' political views and vice-versa. Therefore, in this work, drawing on insights from political science and game studies research, we designed an online casual game that combines the relaxed, playful nonpartisan norms of casual games with corrective information about party supporters' political views that are often misperceived. Through an experiment, we found that playing the game significantly reduces negative feelings toward outparty supporters among Democrats, but not Republicans. It was also effective in improving willingness to talk politics with outparty supporters. Further, we identified psychological reactance as a potential mechanism that affects the effectiveness of depolarization interventions. Finally, our analyses suggest that the game versions with political content were rated to be just as fun to play as a game version without any political content suggesting that, contrary to popular belief, people do like to mix politics and play. 

In studies of misinformation, the distinction between high- and low-credibility publishers is fundamental. However, there is much that we do not know about the relationship between the subject matter and timing of content produced by the two types of publishers. By analyzing the content of several million unique articles published over 28 months, we show that high- and low-credibility publishers operate in distinct news ecosystems. Bursts of news coverage generated by the two types of publishers tend to cover different subject matter at different times, even though fluctuations in their overall news production tend to be highly correlated. Regardless of the mechanism, temporally convergent coverage among low-credibility publishers has troubling implications for American news consumers. 

Past work has explored various ways for online platforms to leverage crowd wisdom for misinformation detection and moderation. Yet, platforms often relegate governance to their communities, and limited research has been done from the perspective of these communities and their moderators. How is misinformation currently moderated in online communities that are heavily self-governed? What role does the crowd play in this process, and how can this process be improved? In this study, we answer these questions through semi-structured interviews with Reddit moderators. We focus on a case study of COVID-19 misinformation. First, our analysis identifies a general moderation workflow model encompassing various processes participants use for handling COVID-19 misinformation. Further, we show that the moderation workflow revolves around three elements: content facticity, user intent, and perceived harm. Next, our interviews reveal that Reddit moderators rely on two types of crowd wisdom for misinformation detection. Almost all participants are heavily reliant on reports from crowds of ordinary users to identify potential misinformation. A second crowd--participants' own moderation teams and expert moderators of other communities--provide support when participants encounter difficult, ambiguous cases. Finally, we use design probes to better understand how different types of crowd signals---from ordinary users and moderators---readily available on Reddit can assist moderators with identifying misinformation. We observe that nearly half of all participants preferred these cues over labels from expert fact-checkers because these cues can help them discern user intent. Additionally, a quarter of the participants distrust professional fact-checkers, raising important concerns about misinformation moderation.