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Unpacking the black box: How to promote citizen engagement through government social media during the COVID-19 crisis.
Chen, Qiang; Min, Chen; Zhang, Wei; Wang, Ge; Ma, Xiaoyue; Evans, Richard.
  • Chen Q; School of Journalism and New Media, Xi'an Jiaotong University, China.
  • Min C; Department of Media and Communication, City University of Hong Kong, Hong Kong, China.
  • Zhang W; College of Public Administration, Huazhong University of Science and Technology, China.
  • Wang G; School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, China.
  • Ma X; College of Public Administration, Central China Normal University, China.
  • Evans R; School of Journalism and New Media, Xi'an Jiaotong University, China.
Comput Human Behav ; 110: 106380, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-47635
ABSTRACT
During times of public crises, governments must act swiftly to communicate crisis information effectively and efficiently to members of the public; failure to do so will inevitably lead citizens to become fearful, uncertain and anxious in the prevailing conditions. This pioneering study systematically investigates how Chinese central government agencies used social media to promote citizen engagement during the COVID-19 crisis. Using data scraped from 'Healthy China', an official Sina Weibo account of the National Health Commission of China, we examine how citizen engagement relates to a series of theoretically relevant factors, including media richness, dialogic loop, content type and emotional valence. Results show that media richness negatively predicts citizen engagement through government social media, but dialogic loop facilitates engagement. Information relating to the latest news about the crisis and the government's handling of the event positively affects citizen engagement through government social media. Importantly, all relationships were contingent upon the emotional valence of each Weibo post.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Comput Human Behav Year: 2020 Document Type: Article Affiliation country: J.chb.2020.106380

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Comput Human Behav Year: 2020 Document Type: Article Affiliation country: J.chb.2020.106380