Spatial Distribution of Hateful Tweets Against Asians and Asian Americans During the COVID-19 Pandemic, November 2019 to May 2020
Spatial Distribution of Hateful Tweets Against Asians and Asian Americans During the COVID-19 Pandemic, November 2019 to May 2020
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DOI：
10.2105/ajph.2021.306653
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发表时间：
2022-04-01
影响因子：
12.7
通讯作者：
Wen, Ming
中科院分区：
文献类型：
--
作者：
Hohl, Alexander;Choi, Moongi;Wen, Ming
目标.说明在COVID-19大流行的早期阶段，美国本土包含反亚洲仇恨语言的地理定位推文的时空分布。方法.我们使用了一组与反映COVID-19和反亚洲仇恨的关键词相匹配的地理定位推文数据集，并使用伯努利模型的时空扫描统计来识别地理集群。结果2020年1月至3月，反亚裔仇恨语言激增。我们在美国本土发现了仇恨的集群。最强的集群由一个县组成（俄亥俄州罗斯县），那里仇恨推文的比例是全国其他地区的312. 13倍。结论. Twitter上的反亚裔仇恨呈现出显著的时空聚集分布。集群的大小、持续时间、强度和位置各不相同，分散在整个美国。公共卫生影响。我们的研究结果可以告知公共卫生和安全的决策者分配资源，以地方为基础的准备和应对流行病引起的种族主义作为公共卫生威胁。(Am J公共卫生。2022; 112（4）：646 - 649. https://doi.org/10.2105/
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)