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How Official Social Media Affected the Infodemic among Adults during the First Wave of COVID-19 in China

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Listed:
  • Huan Liu

    (School of Journalism and New Media, Xi’an Jiaotong University, Xi’an 710049, China)

  • Qiang Chen

    (School of Journalism and New Media, Xi’an Jiaotong University, Xi’an 710049, China)

  • Richard Evans

    (Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada)

Abstract

The COVID-19 pandemic has demonstrated that social media can impact society both positively (e.g., keeping citizens connected and informed) and negatively (e.g., the deliberate spreading of misinformation). This study aims to examine the underlying mechanisms of the relationship between official social media accounts and the infodemic, experienced during the first wave of COVID-19 in China. A theoretical model is proposed to examine how official social media accounts affected the infodemic during this period. In total, 1398 questionnaire responses were collected via WeChat and Tencent QQ, two leading Chinese social media platforms. Data analysis was conducted using Partial Lease Squares Structural Equation Modeling (PLS-SEM), moderation effect analysis, and mediation effect analysis. Results indicate that the Information Quality (IQ) of Official social media accounts (β = −0.294, p < 0.001) has a significant negative effect on the infodemic. Mediation effect analysis revealed that both social support (β = −0.333, 95% Boot CI (−0.388, −0.280)) and information cascades (β = −0.189, 95% Boot CI (−0.227, −0.151)) mediate the relationship between IQ and the infodemic. Moderation effect analysis shows that private social media usage (F = 85.637, p < 0.001) positively moderates the relationship between IQ and the infodemic, while health literacy has a small negative moderation effect on the relationship between IQ and the infodemic. Our findings show that, in the context of Chinese media, official social media accounts act as a major source of information for influencing the infodemic through increasing social support and reducing information cascades for citizens.

Suggested Citation

  • Huan Liu & Qiang Chen & Richard Evans, 2022. "How Official Social Media Affected the Infodemic among Adults during the First Wave of COVID-19 in China," IJERPH, MDPI, vol. 19(11), pages 1-18, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:11:p:6751-:d:829314
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    1. Farooq, Ali & Laato, Samuli & Islam, A.K.M. Najmul & Isoaho, Jouni, 2021. "Understanding the impact of information sources on COVID-19 related preventive measures in Finland," Technology in Society, Elsevier, vol. 65(C).
    2. Francesca Gallè & Elita Anna Sabella & Paolo Roma & Stefano Ferracuti & Giovanna Da Molin & Giusy Diella & Maria Teresa Montagna & Giovanni Battista Orsi & Giorgio Liguori & Christian Napoli, 2021. "Knowledge and Lifestyle Behaviors Related to COVID-19 Pandemic in People over 65 Years Old from Southern Italy," IJERPH, MDPI, vol. 18(20), pages 1-11, October.
    3. Cuiyan Wang & Riyu Pan & Xiaoyang Wan & Yilin Tan & Linkang Xu & Cyrus S. Ho & Roger C. Ho, 2020. "Immediate Psychological Responses and Associated Factors during the Initial Stage of the 2019 Coronavirus Disease (COVID-19) Epidemic among the General Population in China," IJERPH, MDPI, vol. 17(5), pages 1-25, March.
    4. Balakrishnan, Vimala & Ng, Kee S. & Rahim, Hajar Abdul, 2021. "To share or not to share – The underlying motives of sharing fake news amidst the COVID-19 pandemic in Malaysia," Technology in Society, Elsevier, vol. 66(C).
    5. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    6. Kaya, Tugberk, 2020. "The changes in the effects of social media use of Cypriots due to COVID-19 pandemic," Technology in Society, Elsevier, vol. 63(C).
    7. Mohammed, Abdulalem & Ferraris, Alberto, 2021. "Factors influencing user participation in social media: Evidence from twitter usage during COVID-19 pandemic in Saudi Arabia," Technology in Society, Elsevier, vol. 66(C).
    8. Jinling Hua & Rajib Shaw, 2020. "Corona Virus (COVID-19) “Infodemic” and Emerging Issues through a Data Lens: The Case of China," IJERPH, MDPI, vol. 17(7), pages 1-12, March.
    9. Qihua Liu & Shan Huang & Liyi Zhang, 2016. "The influence of information cascades on online purchase behaviors of search and experience products," Electronic Commerce Research, Springer, vol. 16(4), pages 553-580, December.
    10. King, Gary & Pan, Jennifer & Roberts, Margaret E., 2013. "How Censorship in China Allows Government Criticism but Silences Collective Expression," American Political Science Review, Cambridge University Press, vol. 107(2), pages 326-343, May.
    11. Shmueli, Galit & Ray, Soumya & Velasquez Estrada, Juan Manuel & Chatla, Suneel Babu, 2016. "The elephant in the room: Predictive performance of PLS models," Journal of Business Research, Elsevier, vol. 69(10), pages 4552-4564.
    12. Wang, Xia & Yu, Chunling & Wei, Yujie, 2012. "Social Media Peer Communication and Impacts on Purchase Intentions: A Consumer Socialization Framework," Journal of Interactive Marketing, Elsevier, vol. 26(4), pages 198-208.
    13. Kim, Jihyun & Merrill Jr., Kelly & Collins, Chad & Yang, Hocheol, 2021. "Social TV viewing during the COVID-19 lockdown: The mediating role of social presence," Technology in Society, Elsevier, vol. 67(C).
    14. Silvio Vismara, 2018. "Information Cascades among Investors in Equity Crowdfunding," Entrepreneurship Theory and Practice, , vol. 42(3), pages 467-497, May.
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