Unpacking the black box: How to promote citizen engagement through government social media during the COVID-19 crisis
Open accessAbstract
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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Health authorities' communication about risks, treatments, vaccination, and preventive measures may produce anxiety that affects pandemic control.
“In a pandemic situation, the way health authorities communicate to the population the available information on risks, treatments, vaccination opportunities, or the need for preventive measures may lead to levels of anxiety that determine success or failure to control the current pandemic (Asmundson and Taylor, 2020, Chen et al., 2020).”
From Health-related factors of psychological distress during the COVID-19 pandemic among non-health workers in Spain (2021) · cites this paper as Chen et al., 2020
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- Authors
- Chen, Q., Min, C., Zhang, W., Wang, G., Ma, X., & Evans, R.
- Affiliations
- Xi'an Jiaotong University (CN)
- City University of Hong Kong (HK)
- Huazhong University of Science and Technology (CN)
- Central China Normal University (CN)
- Brunel University of London (GB)
- Corresponding author
- Zhang, Wei
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 110
- Pages
- 106380
- ISSN
- 0747-5632 (ISSN-L)
- PMC
- PMC7151317
- OpenAlex
- W3015216621
- Cited by
- 652
- References
- 72
- Citations by year
- 2026: 33 · 2025: 64 · 2024: 90 · 2023: 120 · 2022: 133 · 2021: 164 · 2020: 48
- FWCI
- 139.48
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Computers in Human Behavior (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion)
- Funders
- National Social Science Foundation of China (17CXW026)
- Topics
- Social Sciences › Communication › Public Relations and Crisis Communication
- Social Sciences › Communication › Communication and COVID-19 Impact
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Keywords
- citizen participation, government social media, COVID-19 pandemic, crisis communication, Weibo, media richness, content types, emotional valence
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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