ArticleInternational journal of disaster risk reduction : IJDRR2022
Spatial evolution patterns of public panic on Chinese social networks amidst the COVID-19 pandemic.
Article in International journal of disaster risk reduction : IJDRR, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Comparative Analysis of Prescriptions and Pharmacy Services in Internet-Based Psychiatric Hospital During and After the COVID-19 Pandemic: Retrospective Cross-Sectional Observational Study.Journal of medical Internet research · 2026Observational
- Understanding Public Emotions: Spatiotemporal Dynamics in the Post-Pandemic Era Through Weibo Data.Behavioral sciences (Basel, Switzerland) · 2025Article
- Online profiling of volunteers in public health emergencies: insights from COVID-19 in China.BMC public health · 2025Article
- Impact of exercise on outcomes among Chinese patients with Crohn's disease: a mixed methods study based on social media and the real world.BMC gastroenterology · 2024Article
- Online rumors during the COVID-19 pandemic: co-evolution of themes and emotions.Frontiers in public health · 2024Article
- Modeling emotional contagion in the COVID-19 pandemic: a complex network approach.PeerJ. Computer science · 2023Article
- Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic.International journal of environmental research and public health · 2022Article
- Deep learning modeling of public's sentiments towards temporal evolution of COVID-19 transmission.Applied soft computing · 2022Article
- Pandemic publishing: A bibliometric review of COVID-19 research in the crisis and disaster literature.Risk, hazards & crisis in public policy · 2022Article
- Changes in Doctor-Patient Relationships in China during COVID-19: A Text Mining Analysis.International journal of environmental research and public health · 2022Article
- Spatial-Temporal Pattern Evolution of Public Sentiment Responses to the COVID-19 Pandemic in Small Cities of China: A Case Study Based on Social Media Data Analysis.International journal of environmental research and public health · 2022Article
- Spatiotemporal disparities in regional public risk perception of COVID-19 using Bayesian Spatiotemporally Varying Coefficients (STVC) series models across Chinese cities.International journal of disaster risk reduction : IJDRR · 2022Article
- The fluctuation analysis of public opinion energy: Modeling social group opinion base on the event of social networks.Intelligent systems with applications · 2022Article
- Public Social Media Discussions on Agricultural Product Safety Incidents: Chinese African Swine Fever Debate on Weibo.Frontiers in psychology · 2022Article
- Spatial social network research: a bibliometric analysis.Computational urban science · 2022Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Novel coronavirus pneumonia has had a significant impact on people's lives and psychological health. We developed a stage model to analyse the spatial and temporal distribution of public panic during the two waves of the coronavirus disease 2019 (COVID-19) pandemic. We used tweets with geographic location data from the popular hashtag 'Lockdown Diary' recorded from 23 January to April 8, 2020, and 'Nanjing Outbreak' recorded from 21 July to 1 September 2021 on Weibo. Combining the lexicon-based sentiment analysis and the grounded theory approach, this panic model could explain people's panic and behavioural responses in different areas at different stages of the pandemic. Next, we used the latent Dirichlet allocation topic model to reconfirm the panic model. The results showed that public sentiments fluctuated strongly in the early stages; in this case, panic and prayers were the dominant sentiments. In terms of spatial distribution, public panic showed hierarchical and neighbourhood diffusion, with highly assertive expressions of sentiment at the outbreak sites, economically developed areas, and areas surrounding the outbreak. Most importantly, we considered that public panic was affected by the 17 specific topics extracted based on the perceived and actual distance of the pandemic, thus stimulating the process of panic from minimal, acute, and mild panic to perceived rationality. Consequently, the public's behavioural responses shifted from delayed, negative, and positive, to rational behavioural responses. This study presents a novel approach to explore public panic from both a time and space perspective and provides some suggestions in response to future pandemics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.