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

Thu T. Nguyen, Shaniece Criss, Pallavi Dwivedi, Dina Huang, Jessica Keralis, Erica Hsu, Lynn Phan, Leah H. Nguyen, Isha Yardi, M. Maria Glymour, Amani M. Allen, David H. Chae, Gilbert C. Gee and Quynh C. Nguyen
Additional contact information
Thu T. Nguyen: Department of Family and Community Medicine, University of California San Francisco, San Francisco, CA 94110, USA
Shaniece Criss: Department of Health Sciences, Furman University, Greenville, SC 29613, USA
Pallavi Dwivedi: Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA
Dina Huang: Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA
Jessica Keralis: Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA
Erica Hsu: Department of Public Health Science, University of Maryland, College Park, MD 20742, USA
Lynn Phan: Department of Public Health Science, University of Maryland, College Park, MD 20742, USA
Leah H. Nguyen: Department of Public Health Science, University of Maryland, College Park, MD 20742, USA
Isha Yardi: Department of Public Health Science, University of Maryland, College Park, MD 20742, USA
M. Maria Glymour: Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA 94158, USA
Amani M. Allen: Divisions of Community Health Sciences and Epidemiology, University of California, Berkeley, CA 94704, USA
David H. Chae: Department of Global Community Health and Behavioral Sciences, Tulane School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA
Gilbert C. Gee: Department of Community Health Sciences, University of California, Los Angeles, CA 90095, USA
Quynh C. Nguyen: Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA

IJERPH, 2020, vol. 17, issue 19, 1-13

Abstract: 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 3300 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.

Keywords: social media; minority groups; racial bias; big data; content analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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