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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. 

Social media enables activists to directly communicate with the public and provides a space for movement leaders, participants, bystanders, and opponents to collectively construct and contest narratives. Focusing on Twitter messages from social movements surrounding three issues in 2018-2019 (guns, immigration, and LGBTQ rights), we create a codebook, annotated dataset, and computational models to detect diagnostic (problem identification and attribution), prognostic (proposed solutions and tactics), and motivational (calls to action) framing strategies. We conduct an in-depth unsupervised linguistic analysis of each framing strategy, and uncover cross-movement similarities in associations between framing and linguistic features such as pronouns and deontic modal verbs. Finally, we compare framing strategies across issues and other social, cultural, and interactional contexts. For example, we show that diagnostic framing is more common in replies than original broadcast posts, and that social movement organizations focus much more on prognostic and motivational framing than journalists and ordinary citizens. 

Social media enables the rapid spread of many kinds of information, from pop culture memes to social movements. However, little is known about how information crosses linguistic boundaries. We apply causal inference techniques on the European Twitter network to quantify the structural role and communication influence of multilingual users in cross-lingual information exchange. Overall, multilinguals play an essential role; posting in multiple languages increases betweenness centrality by 13%, and having a multilingual network neighbor increases monolinguals’ odds of sharing domains and hashtags from another language 16-fold and 4-fold, respectively. We further show that multilinguals have a greater impact on diffusing information is less accessible to their monolingual compatriots, such as information from far-away countries and content about regional politics, nascent social movements, and job opportunities. By highlighting information exchange across borders, this work sheds light on a crucial component of how information and ideas spread around the world. 

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. 

Abstract Political and social scientists have been relying extensively on keywords such as hashtags to mine social movement data from social media sites, particularly Twitter. Yet, prior work demonstrates that unrepresentative keyword sets can lead to flawed research conclusions. Numerous keyword expansion methods have been proposed to increase the comprehensiveness of keywords, but systematic evaluations of these methods have been lacking. Our paper fills this gap. We evaluate five diverse keyword expansion techniques (or pipelines) on five representative social movements across two distinct activity levels. Our results guide researchers who aim to use social media keyword searches to mine data. For instance, we show that word embedding-based methods significantly outperform other even more complex and newer approaches when movements are in normal activity periods. These methods are also less computationally intensive. More importantly, we also observe that no single pipeline can identify little more than half of all movement-related tweets when these movements are at their peak mobilization period offline. However, coverage can increase significantly when more than one pipeline is used. This is true even when the pipelines are selected at random. 

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. 

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.