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Open AccessArticle

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

1
Department of Family and Community Medicine, University of California San Francisco, San Francisco, CA 94110, USA
2
Department of Health Sciences, Furman University, Greenville, SC 29613, USA
3
Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA
4
Department of Public Health Science, University of Maryland, College Park, MD 20742, USA
5
Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA 94158, USA
6
Divisions of Community Health Sciences and Epidemiology, University of California, Berkeley, CA 94704, USA
7
Department of Global Community Health and Behavioral Sciences, Tulane School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA
8
Department of Community Health Sciences, University of California, Los Angeles, CA 90095, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(19), 7032; https://doi.org/10.3390/ijerph17197032
Received: 1 September 2020 / Revised: 16 September 2020 / Accepted: 17 September 2020 / Published: 25 September 2020
(This article belongs to the Special Issue Big Data for Public Health Research and Practice)
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. View Full-Text
Keywords: social media; minority groups; racial bias; big data; content analysis social media; minority groups; racial bias; big data; content analysis
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MDPI and ACS Style

Nguyen, T.T.; Criss, S.; Dwivedi, P.; Huang, D.; Keralis, J.; Hsu, E.; Phan, L.; Nguyen, L.H.; Yardi, I.; Glymour, M.M.; Allen, A.M.; Chae, D.H.; Gee, G.C.; Nguyen, Q.C. Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19. Int. J. Environ. Res. Public Health 2020, 17, 7032. https://doi.org/10.3390/ijerph17197032

AMA Style

Nguyen TT, Criss S, Dwivedi P, Huang D, Keralis J, Hsu E, Phan L, Nguyen LH, Yardi I, Glymour MM, Allen AM, Chae DH, Gee GC, Nguyen QC. Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19. International Journal of Environmental Research and Public Health. 2020; 17(19):7032. https://doi.org/10.3390/ijerph17197032

Chicago/Turabian Style

Nguyen, Thu T.; Criss, Shaniece; Dwivedi, Pallavi; Huang, Dina; Keralis, Jessica; Hsu, Erica; Phan, Lynn; Nguyen, Leah H.; Yardi, Isha; Glymour, M. M.; Allen, Amani M.; Chae, David H.; Gee, Gilbert C.; Nguyen, Quynh C. 2020. "Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19" Int. J. Environ. Res. Public Health 17, no. 19: 7032. https://doi.org/10.3390/ijerph17197032

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