Scholarlink

<p>Since the first confirmed case of COVID-19 in the USA on January 19, 2020, the anti-Asian racist and xenophobic rhetoric began to surge on social media, followed by acts of discrimination and harassment against Asians and Asian Americans. In this study, we identified anti-Asian hate language from 17 million geotagged social media posts between December 2019 and August 2022 using an established keyword-based approach, illustrated their spatial and temporal distributions, and explored relationships between socioeconomic and demographic characteristics of places and hate. We found clusters of hate using the spatial relative risk (SPARR) function and used Bayesian hierarchical modeling to draw associations of hate with multiple covariates. We identified 16 clusters, especially in the southern and eastern USA, where anti-Asian hateful tweets surged around March/April 2020. Increased hate was associated with higher COVID-19 death rates, a higher share of the foreign-born population, and a lower share of the Asian population in poverty compared to the White population. There was no indication that spatial structure affected hate. Our results can inform decision-makers in public health and safety for allocating resources for place-based preparedness and response to the pandemic-induced racism as a public health threat.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Spatial Associations of Anti-Asian Hate on Social Media in the USA During COVID-19

  • 期刊论文

  • 发表日期：

    2025年3月18日

查看全文
  • Alexander Hohl,
  • Xiao Huang,
  • Daniel Han,
  • Alexander Yao,
  • Alex Liu,
  • Richard M. Medina,
  • Aggie Yellow Horse,
  • Neng Wan,
  • Zhenlong Li,
  • Ming Wen

摘要

Since the first confirmed case of COVID-19 in the USA on January 19, 2020, the anti-Asian racist and xenophobic rhetoric began to surge on social media, followed by acts of discrimination and harassment against Asians and Asian Americans. In this study, we identified anti-Asian hate language from 17 million geotagged social media posts between December 2019 and August 2022 using an established keyword-based approach, illustrated their spatial and temporal distributions, and explored relationships between socioeconomic and demographic characteristics of places and hate. We found clusters of hate using the spatial relative risk (SPARR) function and used Bayesian hierarchical modeling to draw associations of hate with multiple covariates. We identified 16 clusters, especially in the southern and eastern USA, where anti-Asian hateful tweets surged around March/April 2020. Increased hate was associated with higher COVID-19 death rates, a higher share of the foreign-born population, and a lower share of the Asian population in poverty compared to the White population. There was no indication that spatial structure affected hate. Our results can inform decision-makers in public health and safety for allocating resources for place-based preparedness and response to the pandemic-induced racism as a public health threat.