Who politicized the COVID-19 pandemic on Twitter: cultural identity and Chinese prejudice in a virtual community

Abstract

This research, using the cultural-identity-protective cognition theory and focusing on China as a case study, aims to identify influencers, track information dissemination processes, and recognize the patterns of information and emotional flows within Twitter conversations related to the politicization of the COVID-19 pandemic. 5.3 million Twitter posts were collected related to the coronavirus using keyword searches such as #Wuhan and #Chinesevirus. Out of these, 17,964 tweets were selected as a sample from the most commented tweets by top users. A qualitative textual analysis revealed that Twitter users’ attitudes toward the coronavirus disease and governments’ policies were highly politicized and polarized. Both influencers and general users had the capacity to steer Twitter conversations into the political realm, thus contributing to the politicization and polarization of discussions during the information flow. Notably, opinion leaders, especially among influencers, could politicize and polarize a conversation by initially indicating their political standing in the tweet. In general, Twitter users had the ability to make neutral and objective news information go viral if a large number of Twitter users continue the politicization and polarization processes. Group membership—specifically, political standing—and cultural identities played significant roles in perpetuating a vicious cycle of anger. This cycle amplified identity threats for Twitter users and prompted more prejudicial and polarized comments, especially when China, Chinese people, and the Chinese government were the subjects of blame in the Twitter community.

Keywords:

• Twitter
• COVID-19 pandemic
• large-scale data
• cultural-identity-protective cognition
• influencer
• opinion leader
• politicization
• polarization

Acknowledgments

This paper and the research it presents would not have been possible without the exceptional support of my research assistant, Lefan Xiong, who contributed valuable insights and expertise that greatly aided the research. In particular, her exceptional technological support in developing the customized reply chain using Python and the ETE toolkit was instrumental in establishing the final commenting network sample and visualization for this research. I am immensely grateful for the dedication and contributions she made throughout the course of this research study.

Disclosure statement

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

Additional information

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Yanfang Wu

Yanfang Wu is an assistant professor at the School of Communication at the University of Miami. Her research interests center on social media and digital communication. She concentrates on using both traditional and emerging quantitative and qualitative research techniques, as well as large-scale data analytics tools, to investigate the effects of social media on the public’s cognitive attitudes and behaviors.