News Cycles and Satisfaction With Democracy: How the Pandemic Short-Circuited Media Polarization

Hammoud-Gallego, Omar; Foa, Roberto Stefan; Romero-Vidal, Xavier

Abstract

During the coronavirus pandemic in the United Kingdom, media outlets shifted their focus from divisive political issues to more neutral topics like lifestyle, sports, and entertainment. This study explores how this change in media content relates to partisan divides in satisfaction with democracy. Using data from a representative survey of 201,144 individuals, we linked respondents' perceptions of democratic performance to their daily media exposure. We did so by analysing 1.5 million tweets from British newspapers using a topic modelling algorithm to identify shifts in topic salience and sentiment using sentiment analysis. Our findings reveal a decline in partisan media exposure during the pandemic, associated with increased satisfaction with democracy at both individual and collective levels, and a narrowing of cross-party divides. These results contribute to discussions on affective polarization, the winner-loser gap in democratic evaluation, and media framing effects, highlighting the potential influence of depoliticized news coverage on democratic attitudes.

Citation

Hammoud-Gallego, O., Foa, R. S., & Romero-Vidal, X. (2025). News Cycles and Satisfaction With Democracy: How the Pandemic Short-Circuited Media Polarization. British Journal of Political Science, 55, Article e49. https://doi.org/10.1017/S0007123424000395

• Journal Article Type: Article
• Acceptance Date: Sep 4, 2024
• Online Publication Date: Mar 26, 2025
• Publication Date: Mar 26, 2025
• Deposit Date: Mar 26, 2025
• Publicly Available Date: Mar 27, 2025
• Journal: British Journal of Political Science
• Print ISSN: 0007-1234
• Electronic ISSN: 1469-2112
• Publisher: Cambridge University Press
• Peer Reviewed: Peer Reviewed
• Volume: 55
• Article Number: e49
• DOI: https://doi.org/10.1017/S0007123424000395
• Keywords: satisfaction with democracy; polarization; topic modelling; machine learning; Twitter (X)
• Public URL: https://durham-repository.worktribe.com/output/3742678