Are legislators responsive to the priorities of the public? Research demonstrates a strong correspondence between the issues about which the public cares and the issues addressed by politicians, but conclusive evidence about who leads whom in setting the political agenda has yet to be uncovered. We answer this question with fine-grained temporal analyses of Twitter messages by legislators and the public during the 113th US Congress. After employing an unsupervised method that classifies tweets sent by legislators and citizens into topics, we use vector autoregression models to explore whose priorities more strongly predict the relationship between citizens and politicians. We find that legislators are more likely to follow, than to lead, discussion of public issues, results that hold even after controlling for the agenda-setting effects of the media. We also find, however, that legislators are more likely to be responsive to their supporters than to the general public.

Text has always been an important data source in political science. What has changed in recent years is the feasibility of investigating large amounts of text quantitatively. The internet provides political scientists with more data than their mentors could have imagined, and the research community is providing accessible text analysis software packages, along with training and support. As a result, text-as-data research is becoming mainstream in political science. Scholars are tapping new data sources, they are employing more diverse methods, and they are becoming critical consumers of findings based on those methods. In this article, we first describe the four stages of a typical text-as-data project. We then review recent political science applications and explore one important methodological challenge-topic model instability-in greater detail.

Do images affect online political mobilization? If so, how? These questions are of fundamental importance to scholars of social movements, contentious politics, and political behavior generally. However, little prior work has systematically addressed the role of images in mobilizing online participation in social movements. We first confirm that images have a positive mobilizing effect in the context of online protest activity. We then argue that images are mobilizing because they trigger stronger emotional reactions than text. Building on existing political psychology models, we theorize that images evoking enthusiasm, anger, and fear should be particularly mobilizing, while sadness should be demobilizing. We test the argument through a study of Twitter activity related to a Black Lives Matter protest. We find that both images in general and some of the proposed emotional attributes (enthusiasm and fear) contribute to online participation. The results hold when controlling for alternative theoretical mechanisms for why images should be mobilizing, and for the presence of frequent image features. Our paper provides evidence supporting the broad argument that images increase the likelihood of a protest to spread online while teasing out the mechanisms at play in a new media environment.

Video advertisements, either through television or the Internet, play an essential role in modern political campaigns. For over two decades, researchers have studied television video ads by analyzing the hand-coded data from the Wisconsin Advertising Project and its successor, the Wesleyan Media Project (WMP). Unfortunately, manually coding more than a hundred of variables, such as issue mentions, opponent appearance, and negativity, for many videos is a laborious and expensive process. We propose to automatically code campaign advertisement videos. Applying state-of-theart machine learning methods, we extract various audio and image features from each video file. We show that our machine coding is at least as accurate as human coding for many variables of the WMP data sets. Since many candidates make their advertisement videos available on the Internet, automated coding can dramatically improve the efficiency and scope of campaign advertisement research.

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This study investigates the potential role both untrustworthy and partisan websites play in misinforming audiences by testing whether actual exposure to these sites is associated with political misperceptions. Using a sample of American adult social media users, we match data from individuals’ Internet browser histories with a survey measuring the accuracy of political beliefs. We find that visits to partisan websites are at times related to misperceptions consistent with the political bias of the site. However, we do not find strong evidence that untrustworthy websites consistently relate to false beliefs. There is also little evidence that visits to less partisan, centrist news sites are associated with more accurate political beliefs about these issues, suggesting that exposure to politically neutral news is not necessarily the antidote to misinformation. Results suggest that focusing on partisan news sites—rather than untrustworthy sites—may be fruitful to understanding how media contribute to political misperceptions.

We offer comprehensive evidence of preferences for ideological congruity when people engage with politicians, pundits, and news organizations on social media. Using 4 years of data (2016–2019) from a random sample of 1.5 million Twitter users, we examine three behaviors studied separately to date: (i) following of in-group versus out-group elites, (ii) sharing in-group versus out-group information (retweeting), and (iii) commenting on the shared information (quote tweeting). We find that the majority of users (60%) do not follow any political elites. Those who do follow in-group elite accounts at much higher rates than out-group accounts (90 versus 10%), share information from in-group elites 13 times more frequently than from out-group elites, and often add negative comments to the shared out-group information. Conservatives are twice as likely as liberals to share in-group versus out-group content. These patterns are robust, emerge across issues and political elites, and exist regardless of users’ ideological extremity.

Agenda setting and issue framing research investigates how frames impact public attention, policy decisions, and political outcomes. Social media sites, such as Twitter, provide opportunities to study framing dynamics in an important area of political discourse. We present a method for identifying frames in tweets and measuring their effectiveness. We use topic modeling combined with manual validation to identify recurrent problem frames and topics in thousands of tweets by gun rights and gun control groups following the Marjory Stoneman Douglas High School in Parkland, Florida, shooting. We find that each side used Twitter to advance policy narratives about the problem in Parkland. Gun rights groups' narratives implied that more gun restrictions were not the solution. Their most effective frame focused on officials' failures to enforce existing laws. In contrast, gun control groups portrayed easy access to guns as the problem and emphasized the importance of mobilizing politically to force change.