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Constructing and Communicating COVID-19 Stigma on Twitter: A Content Analysis of Tweets during the Early Stage of the COVID-19 Outbreak

Author

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  • Yachao Li

    (Department of Communication Studies, The College of New Jersey, Ewing, NJ 08628, USA
    Department of Public Health, The College of New Jersey, Ewing, NJ 08628, USA)

  • Sylvia Twersky

    (Department of Public Health, The College of New Jersey, Ewing, NJ 08628, USA)

  • Kelsey Ignace

    (Department of Public Health, The College of New Jersey, Ewing, NJ 08628, USA)

  • Mei Zhao

    (Department of Public Health, The College of New Jersey, Ewing, NJ 08628, USA)

  • Radhika Purandare

    (Department of Communication Studies, The College of New Jersey, Ewing, NJ 08628, USA
    Department of Public Health, The College of New Jersey, Ewing, NJ 08628, USA)

  • Breeda Bennett-Jones

    (Department of Communication Studies, The College of New Jersey, Ewing, NJ 08628, USA)

  • Scott R. Weaver

    (School of Public Health, Georgia State University, Atlanta, GA 30303, USA)

Abstract

This study focuses on stigma communication about COVID-19 on Twitter in the early stage of the outbreak, given the lack of information and rapid global expansion of new cases during this period. Guided by the model of stigma communication, we examine four types of message content, namely mark, group labeling, responsibility, and peril, that are instrumental in forming stigma beliefs and sharing stigma messages. We also explore whether the presence of misinformation and conspiracy theories in COVID-19-related tweets is associated with the presence of COVID-19 stigma content. A total of 155,353 unique COVID-19-related tweets posted between December 31, 2019, and March 13, 2020, were identified, from which 7000 tweets were randomly selected for manual coding. Results showed that the peril of COVID-19 was mentioned the most often, followed by mark, responsibility, and group labeling content. Tweets with conspiracy theories were more likely to include group labeling and responsibility information, but less likely to mention COVID-19 peril. Public health agencies should be aware of the unintentional stigmatization of COVID-19 in public health messages and the urgency to engage and educate the public about the facts of COVID-19.

Suggested Citation

  • Yachao Li & Sylvia Twersky & Kelsey Ignace & Mei Zhao & Radhika Purandare & Breeda Bennett-Jones & Scott R. Weaver, 2020. "Constructing and Communicating COVID-19 Stigma on Twitter: A Content Analysis of Tweets during the Early Stage of the COVID-19 Outbreak," IJERPH, MDPI, vol. 17(18), pages 1-12, September.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:18:p:6847-:d:416022
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    References listed on IDEAS

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    1. Eichelberger, Laura, 2007. "SARS and New York's Chinatown: The politics of risk and blame during an epidemic of fear," Social Science & Medicine, Elsevier, vol. 65(6), pages 1284-1295, September.
    2. Shadi Shahsavari & Pavan Holur & Tianyi Wang & Timothy R. Tangherlini & Vwani Roychowdhury, 2020. "Conspiracy in the time of corona: automatic detection of emerging COVID-19 conspiracy theories in social media and the news," Journal of Computational Social Science, Springer, vol. 3(2), pages 279-317, November.
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    Cited by:

    1. Federica Maria Magarini & Margherita Pinelli & Arianna Sinisi & Silvia Ferrari & Giovanna Laura De Fazio & Gian Maria Galeazzi, 2021. "Irrational Beliefs about COVID-19: A Scoping Review," IJERPH, MDPI, vol. 18(19), pages 1-21, September.
    2. Melissa MacKay & Andrea Cimino & Samira Yousefinaghani & Jennifer E. McWhirter & Rozita Dara & Andrew Papadopoulos, 2022. "Canadian COVID-19 Crisis Communication on Twitter: Mixed Methods Research Examining Tweets from Government, Politicians, and Public Health for Crisis Communication Guiding Principles and Tweet Engagem," IJERPH, MDPI, vol. 19(11), pages 1-12, June.
    3. Jiexun Li & Xiaohui Chang, 2023. "Combating Misinformation by Sharing the Truth: a Study on the Spread of Fact-Checks on Social Media," Information Systems Frontiers, Springer, vol. 25(4), pages 1479-1493, August.
    4. repec:thr:techub:10028:y:2022:i:1:p:451-460 is not listed on IDEAS
    5. Yujia Sui & Bin Zhang, 2021. "Determinants of the Perceived Credibility of Rebuttals Concerning Health Misinformation," IJERPH, MDPI, vol. 18(3), pages 1-17, February.

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