Evidence map›Paper›PMID 40211194›Full record

ArticleBMC public health2025

Insight into public sentiment and demand in China's public health emergency response: a weibo data analysis.

Yanping Wang, Min Wei, Peng Wang, Yiran Gao, Tian Yu, Nan Meng, Huan Liu, Xin Zhang, Kexin Wang, Qunhong Wu

Abstract read
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Yanping Wang *School of Health Management, Harbin Medical University, No 157 Bao Jian Road, Harbin, 150081, China.
Min Wei *School of Health Management, Harbin Medical University, No 157 Bao Jian Road, Harbin, 150081, China.
Peng Wang *School of Health Management, Harbin Medical University, No 157 Bao Jian Road, Harbin, 150081, China.
Yiran GaoSchool of Health Management, Harbin Medical University, No 157 Bao Jian Road, Harbin, 150081, China.
Tian YuSchool of Health Management, Harbin Medical University, No 157 Bao Jian Road, Harbin, 150081, China.
Nan MengSchool of Health Management, Harbin Medical University, No 157 Bao Jian Road, Harbin, 150081, China.
Huan LiuSchool of Health Management, Harbin Medical University, No 157 Bao Jian Road, Harbin, 150081, China.
Xin ZhangSchool of Health Management, Harbin Medical University, No 157 Bao Jian Road, Harbin, 150081, China.
Kexin WangSchool of Health Management, Harbin Medical University, No 157 Bao Jian Road, Harbin, 150081, China. wangkexin1013@126.com.
Qunhong WuSchool of Health Management, Harbin Medical University, No 157 Bao Jian Road, Harbin, 150081, China. wuqunhong@163.com.

Funding

National Natural Science Foundation of China 72361137562
6 · The paper itself

Abstract

backgroundDuring the COVID-19 pandemic, public sentiment and demands have been prominently reflected on social media platforms like Weibo. Understanding these sentiments and demands is crucial for governments, health officials, and policymakers to make effective responses and adjustments.

objectiveThe study aims to analyze public sentiment and identify key demands concerning COVID-19 policies and social issues using Weibo data, providing insights to improve China's policies and legal systems in public health emergencies.

methodsThe study used Python tools to collect public opinion data from Weibo regarding policy adjustments, social issues, and livelihood concerns. A total of 50,249 valid comments on 100 blog posts were collected from December 2019 to October 2023 in China. The SnowNLP algorithm was employed for sentiment analysis, Latent Dirichlet Allocation was used for topic clustering, and sampling coding was applied to further explore public demands by condensing the comment texts.

resultsThe study categorized 100 blog posts into 23 important topics, with average sentiment scores ranging from 0.24 to 0.66. These scores ranging from 0 to 1 reflect sentiment polarity, where lower values indicate more negative public sentiment. The topics of material safety and information security management had the lowest scores, at 0.24 and 0.34, respectively. The analysis further revealed that the 23 topics could be classified into 57 subtopics, and a total of 101 concepts were identified through coding. The study found that public demands fall into five key categories: transportation and travel security, epidemic protection and health security, law building and policy implementation, social services and public demand, and education demand.

conclusionsThe study underscores the complexity of public sentiment during the epidemic, with significant concerns about material safety and information security management. Public demands span basic survival needs to higher-order concerns such as education and legal protections. The findings suggest that policy-making processes must become more responsive, transparent, and equitable, incorporating real-time public feedback and ensuring comprehensive policies and legal systems are in place to address multifaceted public demands effectively.

Indexed as

COVID-19Public HealthPublic OpinionSocial MediaChinaEmergenciesHealth PolicyHumansCOVID-19Latent dirichlet allocationPublic health emergencyPublic opinionSentiment analysis

Identifiers

PMID40211194
PMCPMC11983825

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.