Racial framing contests: How anti-Asian racism and its resistance enacted racial projects during COVID-19

This study examines the dynamics of racial framing contexts taking the case of anti-Asian hate speech and counter-hate speech on social media during the COVID-19 pandemic. Using the COVID-HATE dataset ( n = 2,491,405 tweets posted 15 January 2020 to 26 March 2021), we analyze racial framing contests between movements and counter-movements. Through a mixed-methods approach, we find that: (1) hate frames deployed racial projects characterizing Asians as public health and national security threats, while counter-frames either directly challenged these characterizations or bypassed them to focus on systemic racism and (2) hate and counter-hate movements often “spoke past” each other rather than engaging in direct frame–counterframe dynamics as prevailing theories would predict. Counter-movements did not consistently produce opposing frames for each hate frame but rather developed independent messaging focused on combating racism itself. This study advances our understanding of how both hate and resistance operate through racial projects, with implications for theories of social movements, social media, and racial formation.

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Publication Details

Journal
Big Data & Society
Published
2026-02-26
DOI
https://doi.org/10.1177/20539517261424160
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Racial framing contests: How anti-Asian racism and its resistance enacted racial projects during COVID-19

Alejandra Regla-Vargas, AJ Alvero, Hajar Yazdiha
Big Data & Society
Hate Speech and Cyberbullying Detection
article

Racial framing contests: How anti-Asian racism and its resistance enacted racial projects during COVID-19

Alejandra Regla-Vargas, AJ Alvero, Hajar Yazdiha
article en

Abstract

This study examines the dynamics of racial framing contexts taking the case of anti-Asian hate speech and counter-hate speech on social media during the COVID-19 pandemic. Using the COVID-HATE dataset ( n = 2,491,405 tweets posted 15 January 2020 to 26 March 2021), we analyze racial framing contests between movements and counter-movements. Through a mixed-methods approach, we find that: (1) hate frames deployed racial projects characterizing Asians as public health and national security threats, while counter-frames either directly challenged these characterizations or bypassed them to focus on systemic racism and (2) hate and counter-hate movements often “spoke past” each other rather than engaging in direct frame–counterframe dynamics as prevailing theories would predict. Counter-movements did not consistently produce opposing frames for each hate frame but rather developed independent messaging focused on combating racism itself. This study advances our understanding of how both hate and resistance operate through racial projects, with implications for theories of social movements, social media, and racial formation.

Big Data & SocietyVol. 13(1)
University of Southern California (US), Cornell University (US), California University of Pennsylvania (US)
Openalex Percentile: Top 45%
Hate Speech and Cyberbullying Detection
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Racial framing contests: How anti-Asian racism and its resistance enacted racial projects during COVID-19 — Alejandra Regla-Vargas, AJ Alvero, et al. · Big Data & Society (2026) | TGRS Research Map | TGRS