Focusing on a polarized issue—U.S. gun violence—this study examines agenda setting as an antecedent of political expression on social media. A state-of-the-art machine-learning model was used to analyze news coverage from 25 media outlets—mainstream and partisan. Those results were paired with a two-wave panel survey conducted during the 2018 U.S. midterm elections. Findings show mainstream media shape public opinion about gun violence, which then stimulates expression about the issue on social media. The study also reveals that partisan media’s gun violence coverage has significant cross-cutting effects. Notably, exposure to conservative media will decrease public salience of gun violence, pivot opinion in a more conservative direction, and discourage social media expression; and all of these effects are stronger among liberals.
We propose a new way of imagining and measuring opinions emerging from social media. As people tend to connect with like-minded others and express opinions in response to current events on social media, social media public opinion is naturally occurring, temporally sensitive, and inherently social. Our framework for measuring social media public opinion first samples targeted nodes from a large social graph and identifies homogeneous, interactive, and stable networks of actors, which we call “flocks,” based on social network structure, and then measures and presents opinions of flocks. We apply this framework to Twitter and provide empirical evidence for flocks being meaningful units of analysis and flock membership predicting opinion expression. Through contextualizing social media public opinion by foregrounding the various homogeneous networks it is embedded in, we highlight the need to go beyond the aggregate-level measurement of social media public opinion and study the social dynamics of opinion expression using social media.
more » « less- NSF-PAR ID:
- 10362665
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Journal of Computer-Mediated Communication
- Volume:
- 27
- Issue:
- 1
- ISSN:
- 1083-6101
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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