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From fear to hate: Sources of anti‐Asian sentiment during COVID‐19

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  • Yaoyao Dai
  • Jingjing Gao
  • Benjamin J. Radford

Abstract

Objective This paper seeks to explain and empirically test how a public health crisis, such as the COVID‐19 pandemic, might lead to discriminatory attitudes and behaviors against marginalized groups. Method We identify four causal mechanisms that may account for the increase in anti‐Asian racism during COVID‐19. Using a large data set of geolocated COVID‐19‐related tweets in the U.S., we examine the spatiotemporal pattern of anti‐Asian sentiment on Twitter and test all four mechanisms that link the crisis to the behaviors of “othering” and “scapegoating.” Results We find evidence consistent with the elite cueing, perceived threat, and grievance mechanisms. The president's scapegoating rhetoric significantly increased anti‐Asian hate tweets across all counties. In addition, high COVID‐19 infection rates and grievances from containment policies are also associated with greater numbers of anti‐Asian hate tweets. On the other hand, counties with larger vulnerable populations do not seem to have more anti‐Asian hate tweets. Conclusions We demonstrate that a crisis alone does not necessarily lead to othering behavior, while elite rhetoric can significantly influence the public's opinion and behavior during a crisis.

Suggested Citation

  • Yaoyao Dai & Jingjing Gao & Benjamin J. Radford, 2024. "From fear to hate: Sources of anti‐Asian sentiment during COVID‐19," Social Science Quarterly, Southwestern Social Science Association, vol. 105(3), pages 612-635, May.
  • Handle: RePEc:bla:socsci:v:105:y:2024:i:3:p:612-635
    DOI: 10.1111/ssqu.13387
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    1. Jennifer D. Wu & Gregory A. Huber, 2021. "Partisan Differences in Social Distancing May Originate in Norms and Beliefs: Results from Novel Data," Social Science Quarterly, Southwestern Social Science Association, vol. 102(5), pages 2251-2265, September.
    2. Bell, Andrew & Jones, Kelvyn, 2015. "Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data," Political Science Research and Methods, Cambridge University Press, vol. 3(1), pages 133-153, January.
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