Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19

Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19

Amani M. Allen, Thu T. Nguyen, Shaniece Criss, Pallavi Dwivedi, Dina Huang, Jessica Keralis, Erica Hsu, Lynn Phan, Leah H. Nguyen, Isha Yardi, M. Maria Glymour, David H. Chae, Gilbert C. Gee, Quynh C. Nguyen

Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter's Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019-June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3,300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment.

2020

social media, Big Data, Content Analysis, minority groups, Racial Bias

10.3390/ijerph17197032

Epidemiology and Health

Korean Society of Epidemiology

Medicine