Spatial Distribution of Hateful Tweets Against Asians and Asian Americans During the COVID-19 Pandemic, November 2019 to May 2020

Alexander Hohl, Moongi Choi, Aggie J. Yellow Horse, Richard M. Medina, Neng Wan, Ming Wen

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Objectives. To illustrate the spatiotemporal distribution of geolocated tweets that contain anti-Asian hate language in the contiguous United States during the early phase of the COVID-19 pandemic. Methods. We used a data set of geolocated tweets that match with keywords reflecting COVID-19 and anti-Asian hate and identified geographical clusters using the space-time scan statistic with Bernoulli model. Results. Anti-Asian hate language surged between January and March 2020. We found clusters of hate across the contiguous United States. The strongest cluster consisted of a single county (Ross County, Ohio), where the proportion of hateful tweets was 312.13 times higher than for the rest of the country. Conclusions. Anti-Asian hate on Twitter exhibits a significantly clustered spatiotemporal distribution. Clusters vary in size, duration, strength, and location and are scattered across the entire contiguous United States. Public Health Implications. Our results can inform decision-makers in public health and safety for allocating resources for place-based preparedness and response for pandemic-induced racism as a public health threat. (Am J Public Health. 2022;112(4):646-649. https://doi.org/10.2105/AJPH.2021.306653.

Original languageEnglish (US)
Pages (from-to)646-649
Number of pages4
JournalAmerican journal of public health
Volume112
Issue number4
DOIs
StatePublished - Apr 1 2022

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

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