Many blame partisan news media for polarization in America. This paper examines the effects of liberal, conservative, and centrist news on affective and attitude polarization. To this end, we rely on two studies that combine two-wave panel surveys (N1 = 303, N2 = 904) with twelve months worth of web browsing data submitted by the same participants comprising roughly thirty-eight million visits. We identify news exposure using an extensive list of news domains and develop a machine learning classifier to identify exposure to political news within these domains. The results offer a robust pattern of null findings. Exposure to partisan and centrist news websites—no matter if it is congenial or crosscutting—does not enhance polarization. These null effects also emerge among strong and weak partisans as well as Democrats and Republicans alike. We argue that these null results accurately portray the reality of limited effects of news in the “real world.” Politics and partisan news account for a small fraction of citizens’ online activities, less than 2 percent in our trace data, and are nearly unnoticeable in the overall information and communication ecology of most individuals.

Our study examines Facebook posts containing nine prominent COVID-19 vaccine misinformation topics that circulated on the platform between March 1st, 2020 and March 1st, 2021. We first identify misinformation spreaders and fact checkers, further dividing the latter group into those who repeat misinformation to debunk the false claim and those who share correct information without repeating the misinformation. Our analysis shows that, on Facebook, there are almost as many fact checkers as misinformation spreaders. In particular, fact checkers’ posts that repeat the original misinformation received significantly more comments than posts from misinformation spreaders. However, we found that misinformation spreaders were far more likely to take on central positions in the misinformation URL co-sharing network than fact checkers. This demonstrates the remarkable ability of misinformation spreaders to coordinate communication strategies across topics.

This study aims to identify effective predictors that influence publics’ emotional reactions to COVID-19 vaccine misinformation as well as corrective messages. We collected a large sample of COVID-19 vaccine related misinformation and corrective messages on Facebook as well as the users’ emotional reactions (i.e., emojis) to these messages. Focusing on three clusters of features such as messages’ linguistic features, source characteristics, and messages’ network positions, we examined whether users’ reactions to misinformation and corrective information would differ. We used random forest models to identify the most salient predictors among over 70 predictors for both types of messages. Our analysis found that for misinformation, political ideology of the message source was the most salient feature that predicted anxious and enthusiastic reactions, followed by message features that highlight personal concerns and messages’ network positions. For corrective messages, while the sources’ ideology was still key to raising anxiety, the most important feature for triggering enthusiasm was the messages’ network positions and message quality.

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Guided by the moral foundation theory, this study examined how moral framing interacted with local constituents' ideological leaning to affect public engagement outcomes of government agencies' COVID-19 vaccine communication on Facebook. We analyzed a dataset of over 5,000 U.S. government agencies' Facebook posts on COVID-19 vaccines in 2021 (𝑁 = 70, 671), assessed their use of moral language using a newly developed computational method, and examined how political divide manifests itself at the collective level. Findings from both fixed and random effects models suggest that: 1) the use of moral language is positively associated with public engagement outcomes on government agencies' social media accounts; 2) five types of moral foundations have distinct effects on three types of public engagement (affective, cognitive, and retransmission); 3) moral foundations and local politics interact to affect public engagement, in that followers of government agencies in liberal states/counties prefer messages emphasizing the care/harm and fairness/cheating dimensions while those in conservative states/counties prefer the loyalty/betrayal dimension. The study demonstrates how a strategic employment of moral language can contribute to public engagement of government agencies' mass communication campaigns.