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

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  • 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)

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.

Suggested Citation

  • 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. G, 2020. "Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19," IJERPH, MDPI, vol. 17(19), pages 1-13, September.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:19:p:7032-:d:419899
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    References listed on IDEAS

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    1. Thu T. Nguyen & Shaniece Criss & Amani M. Allen & M. Maria Glymour & Lynn Phan & Ryan Trevino & Shrikha Dasari & Quynh C. Nguyen, 2019. "Pride, Love, and Twitter Rants: Combining Machine Learning and Qualitative Techniques to Understand What Our Tweets Reveal about Race in the US," IJERPH, MDPI, vol. 16(10), pages 1-19, May.
    2. Diane Lauderdale, 2006. "Birth outcomes for Arabic-named women in California before and after September 11," Demography, Springer;Population Association of America (PAA), vol. 43(1), pages 185-201, February.
    3. Yin Paradies & Jehonathan Ben & Nida Denson & Amanuel Elias & Naomi Priest & Alex Pieterse & Arpana Gupta & Margaret Kelaher & Gilbert Gee, 2015. "Racism as a Determinant of Health: A Systematic Review and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-48, September.
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    Cited by:

    1. Ian Peacock & Emily Ryo, 2022. "A study of pandemic and stigma effects in removal proceedings," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 19(3), pages 560-593, September.
    2. Mazhar Javed Awan & Muhammad Haseeb Bilal & Awais Yasin & Haitham Nobanee & Nabeel Sabir Khan & Azlan Mohd Zain, 2021. "Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach," IJERPH, MDPI, vol. 18(19), pages 1-16, September.
    3. Shaniece Criss & Thu T. Nguyen & Samantha Norton & Imaya Virani & Eli Titherington & Emma Lou Tillmanns & Courtney Kinnane & Gabrielle Maiolo & Anne B. Kirby & Gilbert C. Gee, 2021. "Advocacy, Hesitancy, and Equity: Exploring U.S. Race-Related Discussions of the COVID-19 Vaccine on Twitter," IJERPH, MDPI, vol. 18(11), pages 1-12, May.
    4. Jiepin Cao & Chiyoung Lee & Wenyang Sun & Jennie C. De Gagne, 2022. "The #StopAsianHate Movement on Twitter: A Qualitative Descriptive Study," IJERPH, MDPI, vol. 19(7), pages 1-11, March.
    5. Glenda Ballantyne & Vincent Giarrusso, 2023. "Asian Australian Experiences of Racism During the COVID-19 Pandemic in Victoria: a Preliminary Analysis," Journal of International Migration and Integration, Springer, vol. 24(3), pages 1437-1453, September.
    6. Galetsi, Panagiota & Katsaliaki, Korina & Kumar, Sameer, 2022. "The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19," Social Science & Medicine, Elsevier, vol. 301(C).
    7. Acolin, Jessica & Fishman, Paul, 2023. "Beyond the biomedical, towards the agentic: A paradigm shift for population health science," Social Science & Medicine, Elsevier, vol. 326(C).

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