The Geography of Anti-Asian Hate on Twitter during the COVID-19 Pandemic, November 2019 to May 2020

Keywords: hate, Asian, SaTscan, COVID-19, Twitter
Session Type: Virtual Paper Abstract
Day: Tuesday
Session Start / End Time: 3/1/2022 03:40 PM (Eastern Time (US & Canada)) - 3/1/2022 05:00 PM (Eastern Time (US & Canada))
Room: Virtual 13

Abstract

Since the first confirmed case of COVID-19 in the US 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 the US. Our objective is to illustrate the spatiotemporal distribution of geolocated tweets that contain anti-Asian hate language in the contiguous US during the early phase of the COVID-19 pandemic. We obtained a dataset 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. Our results indicate that Anti-Asian hate language on Twitter surged between January and March 2020, exhibiting a significantly clustered spatiotemporal distribution. Clusters vary in size, duration, strength and location and are scattered across the entire contiguous US, including high-population-density cities, their suburbs, as well as rural places. The strongest cluster consists of a single county (Ross County, OH), where the proportion of hateful tweets was 312.13 times higher than for the rest of the country. Our results can inform decision makers in public health and safety for allocating resources for place-based preparedness and response for the pandemic-induced racism as a public health threat.