Politics Go “Viral”: A Computational Text Analysis of the Public Attribution and Attitude Regarding the COVID-19 Crisis and Governmental Responses on Twitter

Weilu Zhang, Lingshu Hu, Jihye Park

Research output: Contribution to journalArticlepeer-review

Abstract

The U.S. confronts an unprecedented public health crisis, the COVID-19 pandemic, in the presidential election year in 2020. In such a compound situation, a real-time dynamic examination of how the general public ascribe the crisis responsibilities taking account to their political ideologies is helpful for developing effective strategies to manage the crisis and diminish hostility toward particular groups caused by polarization. Social media, such as Twitter, provide platforms for the public’s COVID-related discourse to form, accumulate, and visibly present. Meanwhile, those features also make social media a window to monitor the public responses in real-time. This research conducted a computational text analysis of 2,918,376 tweets sent by 829,686 different U.S. users regarding COVID-19 from January 24 to May 25, 2020. Results indicate that the public’s crisis attribution and attitude toward governmental crisis responses are driven by their political identities. One crisis factor identified by this study (i.e., threat level) also affects the public’s attribution and attitude polarization. Additionally, we note that pandemic fatigue was identified in our findings as early as in March 2020. This study has theoretical, practical, and methodological implications informing further health communication in a heated political environment.

Original languageEnglish
JournalSocial Science Computer Review
DOIs
StateAccepted/In press - 2022

Bibliographical note

Publisher Copyright:
© The Author(s) 2022.

Keywords

  • COVID-19
  • Twitter
  • machine learning
  • neural network
  • political orientation
  • public health crisis management

ASJC Scopus subject areas

  • Social Sciences (all)
  • Computer Science Applications
  • Library and Information Sciences
  • Law

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