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What makes an online help-seeking message go far during the COVID-19 crisis in mainland China? A multilevel regression analysis.
Chen, Anfan; Ng, Aaron; Xi, Yipeng; Hu, Yong.
  • Chen A; School of Humanity and Social Science, University of Science and Technology of China, Anhui Province, China.
  • Ng A; Business, Communication and Design Cluster, Singapore Institute of Technology, Singapore.
  • Xi Y; School of Media and Communication, Shanghai Jiao Tong University, P.R.China.
  • Hu Y; Department of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
Digit Health ; 8: 20552076221085061, 2022.
Article in English | MEDLINE | ID: covidwho-1759668
ABSTRACT
Various studies have explored the underlying mechanisms that enhance the overall reach of a support-seeking message on social media networks. However, little attention has been paid to an under-examined structural feature of help-seeking message diffusion, information diffusion depth, and how support-seeking messages can traverse vertically into social media networks to reach other users who are not directly connected to the help-seeker. Using the multilevel regression to analyze 705 help-seeking posts regarding COVID-19 on Sina Weibo, we examined sender, content, and environmental factors to investigate what makes help-seeking messages traverse deeply into social media networks. Results suggested that bandwagon cues, anger, instrumental appeal, and intermediate self-disclosure facilitate the diffusion depth of help-seeking messages. However, the effects of these factors were moderated by the epidemic severity. Implications of the findings on support-seeking behavior and narrative strategies on social media were also discussed.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Digit Health Year: 2022 Document Type: Article Affiliation country: 20552076221085061

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Digit Health Year: 2022 Document Type: Article Affiliation country: 20552076221085061