Examining U.S. Newspapers’ Partisan Bias in COVID-19 News Using Computational Methods

ABSTRACT

The COVID-19 pandemic has become a partisan political issue instead of purely a public health issue in the U.S. Partisan media bias leads to conflicting messages and drastic differences in preventive behaviors and risk perceptions between Democrats and Republicans. Guided by partisan media bias literature and framing theory, this study examined partisan media bias in the U.S. national and local newspapers regarding COVID-19 using computational methods. It visualized the trends of COVID-19 news articles published by left-leaning, least biased, and right-leaning media as well as revealed frames that were used in partisan media to report COVID-19. Findings demonstrated that partisan media covered certain COVID-19 frames more frequently than others. Even though left-leaning, least biased, and right-leaning media did not differ in the likelihood of publishing COVID-19 articles and they did not publish a significantly different number of COVID-19 articles, partisan media used each COVID-19 frame significantly differently. Specifically, least biased media was more likely than left-leaning media and right-leaning media to discuss the stay-at-home order. Other frames were not significantly differently applied by different partisan media. Implications for COVID-19 news reporting and message design as well as the lessons for politics and health policy are provided.

KEYWORDS:

• COVID-19
• Framing
• Partisan bias
• Media bias
• Computational methods

Acknowledgements

I would like to thank Hao Guo for her assistance in data analysis. I deeply appreciate the insightful comments from the editor and the reviewers.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Zhan Xu

Zhan Xu is Assistant Professor at the School of Communication and Faculty in the Interdisciplinary Health Ph.D. Program at Northern Arizona University. Her areas of expertise include health communication, crisis and risk communication, and new technologies, with a focus on social media analytics and big data analysis utilizing computational methods. Recent publications have appeared in journals such as Information, Communication and Society, Health Communication, and Internet Research.