What Makes Gun Violence a (Less) Prominent Issue? A Computational Analysis of Compelling Arguments and Selective Agenda Setting

ABSTRACT

Despite several transient spikes in response to the deadliest mass shootings, the U.S. population continues to perceive gun violence as less important than other issues, and public opinion remains divided along partisan lines. Drawing upon literature of compelling arguments and partisan media, this study investigates what kind of news framing—episodic framing that focuses on individual stories or thematic framing that emphasizes broader context—makes gun violence a more or less prominent issue. Specifically, this study uses the state-of-the-art machine-learning model BERT to examine 25 news media outlets’ coverage of gun violence, and then pairs the results with a two-wave panel survey conducted during the 2018 U.S. midterm elections. Results demonstrate that episodic framing of gun violence in the elite, mainstream media increased the issue salience among conservatives. However, exposure to episodically framed coverage of gun violence in like-minded partisan media made conservatives believe the issue was less important.

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website.

Notes

¹ Overall, the Democratic and Republican Parties correspond closely with liberal and conservative ideologies, respectively. However, it is not a 100% correlation. In this paper, we use the terms “conservative” and “liberal” to report our own analysis, and use “Democratic” and “Republican” if these are used in the cited studies.

² Precision is the ratio of true positives to the total predicted positive observations. In this case, precision measures, from all of the news headlines identified using keywords, how many are indeed about gun violence. Recall is the ratio of true positives to all observations in the actual case. In this case, recall measures, from all of the relevant news headlines, how many are retrieved by the keywords.

³ The SEM model also confirmed that no CAs effect was found on liberals.

Additional information

Funding

This study is supported in part by the U.S. NSF grant [1838193].

Notes on contributors

Lei Guo

Lei Guo (Ph.D., The University of Texas at Austin) is an assistant professor in the Emerging Media Studies Division at College of Communication, Boston University. Her research focuses on the development of media effects theories, computational social science methodologies, and emerging media and democracy in the United States and China.

Kate Mays

Kate Mays is a Ph.D. candidate in the Emerging Media Studies Division at College of Communication, Boston University. Her research interests include networked publics, technological affordances of digital platforms, and emerging media effects.

Yiyan Zhang

Yiyan Zhang is a Ph.D. candidate in the Emerging Media Studies Division at College of Communication, Boston University. Her research focuses on the civic and political influence of digital media.

Derry Wijaya

Derry Wijaya (Ph.D., Carnegie Mellon University) is a assistant professor in the Department of Computer Science at Boston University. She conducts research in natural language processing and knowledge bases, with a focus on machine learning and deep learning applications on these research areas.

Margrit Betke

Margrit Betke (Ph.D., Massachusetts Institute of Technology) is a professor in the Department of Computer Science at Boston University. Her research interests are in computer vision and human computation.