TY - JOUR
T1 - Spatial Distribution of Hateful Tweets against Asians and Asian Americans during the COVID-19 Pandemic, November 2019 to May 2020
AU - Hohl, Alexander
AU - Choi, Moongi
AU - Yellow Horse, Aggie J.
AU - Medina, Richard M.
AU - Wan, Neng
AU - Wen, Ming
N1 - Funding Information:
This work was supported by the Immunology, Inflammation and Infectious Diseases Initiative and the Office of the Vice President for Research of the University of Utah.
Publisher Copyright:
© 2022 American Public Health Association Inc.. All rights reserved.
PY - 2022/4
Y1 - 2022/4
N2 - 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.
AB - 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.
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U2 - 10.2105/AJPH.2021.306653
DO - 10.2105/AJPH.2021.306653
M3 - Article
C2 - 35319960
AN - SCOPUS:85126859232
SN - 0090-0036
VL - 112
SP - 646
EP - 649
JO - American journal of public health
JF - American journal of public health
IS - 4
ER -