Disease, Scapegoating, and Social Contexts: Examining Social Contexts of the Support for Racist Naming of COVID-19 on Twitter

Abstract

In early 2020, when COVID-19 began to spread in the United States, many Twitter users called it the “Chinese virus,” blaming racial outgroups for the pandemic. I collected tweets containing the “Chinese virus” derivatives posted from March to August 2020 by users within the United States and created a data set with 141,290 tweets published by 50,695 users. I calculated the ratio of users who supported the racist naming of COVID-19 per county and merged Twitter data with the county-level census. Multilevel regression models show that counties with higher COVID-19 mortality or infection rates have more support for the racist naming. Second, the mortality and infection rates effects are stronger in counties with faster minority growth. Moreover, it is mainly in poor counties that minority growth enlarges the effects of infection and mortality rates. These findings relate to the theories on disease-induced xenophobia and the debate between conflict and contact theories.