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Social Bots’ Role in the COVID-19 Pandemic Discussion on Twitter

Author

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  • Yaming Zhang

    (School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
    Internet Plus and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China)

  • Wenjie Song

    (School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
    Internet Plus and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China)

  • Jiang Shao

    (School of Economics and Management, Yanshan University, Qinhuangdao 066004, China)

  • Majed Abbas

    (School of Economics and Management, Yanshan University, Qinhuangdao 066004, China)

  • Jiaqi Zhang

    (School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
    Internet Plus and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China)

  • Yaya H. Koura

    (School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
    School of Foreign Languages, Yanshan University, Qinhuangdao 066004, China)

  • Yanyuan Su

    (School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
    Internet Plus and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China)

Abstract

Social bots have already infiltrated social media platforms, such as Twitter, Facebook, and so on. Exploring the role of social bots in discussions of the COVID-19 pandemic, as well as comparing the behavioral differences between social bots and humans, is an important foundation for studying public health opinion dissemination. We collected data on Twitter and used Botometer to classify users into social bots and humans. Machine learning methods were used to analyze the characteristics of topic semantics, sentiment attributes, dissemination intentions, and interaction patterns of humans and social bots. The results show that 22% of these accounts were social bots, while 78% were humans, and there are significant differences in the behavioral characteristics between them. Social bots are more concerned with the topics of public health news than humans are with individual health and daily lives. More than 85% of bots’ tweets are liked, and they have a large number of followers and friends, which means they have influence on internet users’ perceptions about disease transmission and public health. In addition, social bots, located mainly in Europe and America countries, create an “authoritative” image by posting a lot of news, which in turn gains more attention and has a significant effect on humans. The findings contribute to understanding the behavioral patterns of new technologies such as social bots and their role in the dissemination of public health information.

Suggested Citation

  • Yaming Zhang & Wenjie Song & Jiang Shao & Majed Abbas & Jiaqi Zhang & Yaya H. Koura & Yanyuan Su, 2023. "Social Bots’ Role in the COVID-19 Pandemic Discussion on Twitter," IJERPH, MDPI, vol. 20(4), pages 1-21, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:4:p:3284-:d:1067032
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    References listed on IDEAS

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    1. Charles Cho & Martin Martens & Hakkyun Kim & Michelle Rodrigue, 2011. "Astroturfing Global Warming: It Isn’t Always Greener on the Other Side of the Fence," Journal of Business Ethics, Springer, vol. 104(4), pages 571-587, December.
    2. Chengcheng Shao & Pik-Mai Hui & Lei Wang & Xinwen Jiang & Alessandro Flammini & Filippo Menczer & Giovanni Luca Ciampaglia, 2018. "Anatomy of an online misinformation network," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-23, April.
    3. Adam Badawy & Emilio Ferrara, 2018. "The rise of Jihadist propaganda on social networks," Journal of Computational Social Science, Springer, vol. 1(2), pages 453-470, September.
    4. Massimo Stella & Emilio Ferrara & Manlio De Domenico, 2018. "Bots increase exposure to negative and inflammatory content in online social systems," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(49), pages 12435-12440, December.
    5. Joshua Uyheng & Kathleen M. Carley, 2020. "Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines," Journal of Computational Social Science, Springer, vol. 3(2), pages 445-468, November.
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