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Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic

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  • Zixuan Weng

    (School of Journalism and Communication, Jinan University, Guangzhou 510632, China)

  • Aijun Lin

    (School of Journalism and Communication, Jinan University, Guangzhou 510632, China)

Abstract

Social media is not only an essential platform for the dissemination of public health-related information, but also an important channel for people to communicate during the COVID-19 pandemic. However, social bots can interfere with the social media topics that humans follow. We analyzed and visualized Twitter data during the prevalence of the Wuhan lab leak theory and discovered that 29% of the accounts participating in the discussion were social bots. We found evidence that social bots play an essential mediating role in communication networks. Although human accounts have a more direct influence on the information diffusion network, social bots have a more indirect influence. Unverified social bot accounts retweet more, and through multiple levels of diffusion, humans are vulnerable to messages manipulated by bots, driving the spread of unverified messages across social media. These findings show that limiting the use of social bots might be an effective method to minimize the spread of conspiracy theories and hate speech online.

Suggested Citation

  • Zixuan Weng & Aijun Lin, 2022. "Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(24), pages 1-17, December.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16376-:d:995607
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    References listed on IDEAS

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    1. Gorodnichenko, Yuriy & Pham, Tho & Talavera, Oleksandr, 2021. "Social media, sentiment and public opinions: Evidence from #Brexit and #USElection," European Economic Review, Elsevier, vol. 136(C).
    2. Ho-Chun Herbert Chang & Emilio Ferrara, 2022. "Comparative analysis of social bots and humans during the COVID-19 pandemic," Journal of Computational Social Science, Springer, vol. 5(2), pages 1409-1425, November.
    3. Andrea Castro-Martinez & Paula Méndez-Domínguez & Aimiris Sosa Valcarcel & Joaquín Castillo de Mesa, 2021. "Social Connectivity, Sentiment and Participation on Twitter during COVID-19," IJERPH, MDPI, vol. 18(16), pages 1-19, August.
    4. Wentao Xu & Kazutoshi Sasahara, 2022. "Characterizing the roles of bots on Twitter during the COVID-19 infodemic," Journal of Computational Social Science, Springer, vol. 5(1), pages 591-609, May.
    5. Thomas Marlow & Sean Miller & J. Timmons Roberts, 2021. "Bots and online climate discourses: Twitter discourse on President Trump’s announcement of U.S. withdrawal from the Paris Agreement," Climate Policy, Taylor & Francis Journals, vol. 21(6), pages 765-777, July.
    6. Cynthia Chew & Gunther Eysenbach, 2010. "Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-13, November.
    7. Ana Pérez-Escoda & Carlos Jiménez-Narros & Marta Perlado-Lamo-de-Espinosa & Luis Miguel Pedrero-Esteban, 2020. "Social Networks’ Engagement During the COVID-19 Pandemic in Spain: Health Media vs. Healthcare Professionals," IJERPH, MDPI, vol. 17(14), pages 1-17, July.
    8. Yuehua Zhao & Jin Zhang & Min Wu, 2019. "Finding Users’ Voice on Social Media: An Investigation of Online Support Groups for Autism-Affected Users on Facebook," IJERPH, MDPI, vol. 16(23), pages 1-13, November.
    9. Kristin E. Gibson & Catherine E. Sanders & Alexa J. Lamm, 2021. "Information Source Use and Social Media Engagement: Examining their Effects on Origin of COVID-19 Beliefs," SAGE Open, , vol. 11(4), pages 21582440211, November.
    10. 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.
    11. 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.
    12. Sandra González-Bailón & Manlio De Domenico, 2021. "Bots are less central than verified accounts during contentious political events," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(11), pages 2013443118-, March.
    13. Wen Shi & Diyi Liu & Jing Yang & Jing Zhang & Sanmei Wen & Jing Su, 2020. "Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter," IJERPH, MDPI, vol. 17(22), pages 1-18, November.
    14. Kai-Cheng Yang & Emilio Ferrara & Filippo Menczer, 2022. "Botometer 101: social bot practicum for computational social scientists," Journal of Computational Social Science, Springer, vol. 5(2), pages 1511-1528, November.
    15. Yunkai Zhai & Xin Song & Yajun Chen & Wei Lu, 2022. "A Study of Mobile Medical App User Satisfaction Incorporating Theme Analysis and Review Sentiment Tendencies," IJERPH, MDPI, vol. 19(12), pages 1-19, June.
    16. Chengcheng Shao & Giovanni Luca Ciampaglia & Onur Varol & Kai-Cheng Yang & Alessandro Flammini & Filippo Menczer, 2018. "The spread of low-credibility content by social bots," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    17. 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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