An Analysis of Temporal Trends in Anti-Asian Hate and Counter-Hate on Twitter During the COVID-19 Pandemic

Brittany Wheeler, Arizona State University
Seong Jung, Arizona State University
Deborah Hall, Arizona State University
Monika Purohit, Loyola University Chicago
Yasin N. Silva, Loyola University Chicago

Abstract

Recent studies have documented increases in anti-Asian hate throughout the COVID-19 pandemic. Yet relatively little is known about how anti-Asian content on social media, as well as positive messages to combat the hate, have varied over time. In this study, we investigated temporal changes in the frequency of anti-Asian and counter-hate messages on Twitter during the first 16 months of the COVID-19 pandemic. Using the Twitter Data Collection Application Programming Interface, we queried all tweets from January 30, 2020 to April 30, 2021 that contained specific anti-Asian (e.g., #chinavirus, #kungflu) and counter-hate (e.g., #hateisavirus) keywords. From this initial data set, we extracted a random subset of 1,000 Twitter users who had used one or more anti-Asian or counter-hate keywords. For each of these users, we calculated the total number of anti-Asian and counter-hate keywords posted each month. Latent growth curve analysis revealed that the frequency of anti-Asian keywords fluctuated over time in a curvilinear pattern, increasing steadily in the early months and then decreasing in the later months of our data collection. In contrast, the frequency of counter-hate keywords remained low for several months and then increased in a linear manner. Significant between-user variability in both anti-Asian and counter-hate content was observed, highlighting individual differences in the generation of hate and counter-hate messages within our sample. Together, these findings begin to shed light on longitudinal patterns of hate and counter-hate on social media during the COVID-19 pandemic.