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Dialogic communication on local government social media during the first wave of COVID-19: Evidence from the health commissions of prefecture-level cities in China.
Chen, Qiang; Zhang, Yangyi; Liu, Huan; Zhang, Wei; Evans, Richard.
  • Chen Q; School of Journalism and New Media, Xi'an Jiaotong University, Xi'an, China.
  • Zhang Y; School of Journalism and New Media, Xi'an Jiaotong University, Xi'an, China.
  • Liu H; School of Journalism and New Media, Xi'an Jiaotong University, Xi'an, China.
  • Zhang W; School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, China.
  • Evans R; Faculty of Computer Science, Dalhousie University, Halifax, Canada.
Comput Human Behav ; 143: 107715, 2023 Jun.
Article in English | MEDLINE | ID: covidwho-2239723
ABSTRACT
Although some scholars have explored the level and determinants of Dialogic Communication on Government Social Media (DCGSM), none have conducted their studies in the context of public crisis. The current study contributes to the understanding on DCGSM by 16,822 posts crawled from the official Sina Weibo accounts of 104 Chinese health commissions in prefecture-level cities during the first wave of the COVID-19 pandemic. Results show that Chinese local government agencies have great variations in their DCGSM during the pandemic and the overall performance is poor. Furthermore, Chinese local governments prefer to conserve visitors and generate return visits, rather than dialogic loops development and the usefulness of information enhancement. The findings suggest that both public pressure and peer pressure contribute to the DCGSM of Chinese local governments during the public health crisis. In addition, the effect of public pressure is stronger than that of the peer pressure, indicating that local government agencies have experienced more demand-pull DCGSM.
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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Comput Human Behav Year: 2023 Document Type: Article Affiliation country: J.chb.2023.107715

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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Comput Human Behav Year: 2023 Document Type: Article Affiliation country: J.chb.2023.107715