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Debates over the role of Traditional Chinese Medicine on COVID-19: A computational comparison between professionals and laypersons in Chinese online knowledge community

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  • Li, Jinhui
  • Shi, Wen

Abstract

Leveraging a large collection of textual data (N = 21,539) from a Chinese online community, we employed structural topic modeling to investigate the thematic disparities between professionals and laypersons, regarding the effectiveness of Traditional Chinese Medicine (TCM) on COVID-19. Findings reveal that laypersons are the dominant communicators in terms of discussion volume, who often focus on relevant news events, societal or political aspects of TCM. In contrast, professionals keep concentrating on issues related to medical expertise, and do not shift attentions as frequent as laypersons. Despite the dominant influence of professionals on laypersons’ agenda, two-way agenda interactions identified confirm that lay public is empowered to negotiate with elite professionals under certain topics. Our results provide novel insights into the dynamic nature of attentions, behaviors, and relations among prominent communication actors, and encourage future research to examine the individual-level and societal-level impacts of these constructs in the emerging online media landscape.

Suggested Citation

  • Li, Jinhui & Shi, Wen, 2024. "Debates over the role of Traditional Chinese Medicine on COVID-19: A computational comparison between professionals and laypersons in Chinese online knowledge community," Social Science & Medicine, Elsevier, vol. 361(C).
    • Handle: RePEc:eee:socmed:v:361:y:2024:i:c:s0277953624008207
    DOI: 10.1016/j.socscimed.2024.117366
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    1. Neil F. Johnson & Nicolas Velásquez & Nicholas Johnson Restrepo & Rhys Leahy & Nicholas Gabriel & Sara El Oud & Minzhang Zheng & Pedro Manrique & Stefan Wuchty & Yonatan Lupu, 2020. "The online competition between pro- and anti-vaccination views," Nature, Nature, vol. 582(7811), pages 230-233, June.
    2. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    3. King, Gary & Pan, Jennifer & Roberts, Margaret E., 2017. "How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, Not Engaged Argument," American Political Science Review, Cambridge University Press, vol. 111(3), pages 484-501, August.
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