Does Citizen Engagement With Government Social Media Accounts Differ During the Different Stages of Public Health Crises? An Empirical Examination of the COVID-19 Pandemic
Bibliographic record
Abstract
Background: The COVID-19 pandemic has created one of the greatest challenges to humankind, developing long-lasting socio-economic impacts on our health and wellbeing, employment, and global economy. Citizen engagement with government social media accounts has proven crucial for the effective communication and management of public health crisis. Although much research has explored the societal impact of the pandemic, extant literature has failed to create a systematic and dynamic model that examines the formation mechanism of citizen engagement with government social media accounts at the different stages of the COVID-19 pandemic. This study fills this gap by employing the Heuristic-Systematic Model and investigating the effects of the heuristic clues including social media capital, information richness, language features, dialogic loop, and the systematic clue including content types, on citizen engagement with government social media across three different stages of the pandemic, employing the moderating role of emotional valence. Methods: The proposed model is validated by scraping 16,710 posts from 22 provincial and municipal government micro-blog accounts in the Hubei province, China. Results: Results show that the positive effects of social media capital on citizen engagement were observed at all stages. However, the effects of information richness, language features, dialogic loop, and content types, and the moderating effect of emotional valence, varied across the different pandemic development stages. Conclusions: The findings provide suggestions for the further effective use of government social media, and better cope with crises. Government agencies should pay attention to the content and form of information shared, using technical means to analyze the information needs of citizens at different stages of public health emergencies, understanding the content most concerned by citizens, and formulating the content type of posts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".