Evidence map›Paper›PMID 40111382›Full record

ArticleJMIR medical informatics2025

Public Attitudes Toward Violence Against Doctors: Sentiment Analysis of Chinese Users.

Yuwen Zheng, Meirong Tian, Jingjing Chen, Lei Zhang, Jia Gao, Xiang Li, Jin Wen, Xing Qu

Abstract read
In one paragraph

Article in JMIR medical informatics, 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

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yuwen ZhengInstitute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China.ORCID 0009-0009-0157-5221
Meirong TianInstitute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China.ORCID 0009-0004-8531-7281
Jingjing ChenInstitute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China.ORCID 0009-0009-9815-9362
Lei ZhangInstitute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0003-4093-5233
Jia GaoInstitute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China.ORCID 0009-0005-1913-9971
Xiang LiInstitute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China.ORCID 0009-0009-5881-8925
Jin Wen *Institute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0002-1502-7244
Xing Qu *Institute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0002-7696-7192

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundViolence against doctors attracts the public's attention both online and in the real world. Understanding how public sentiment evolves during such crises is essential for developing strategies to manage emotions and rebuild trust.

objectiveThis study aims to quantify the difference in public sentiment based on the public opinion life cycle theory and describe how public sentiment evolved during a high-profile crisis involving violence against doctors in China.

methodsThis study used the term frequency-inverse document frequency (TF-IDF) algorithm to extract key terms and create keyword clouds from textual comments. The latent Dirichlet allocation (LDA) topic model was used to analyze the thematic trends and shifts within public sentiment. The integrated Chinese Sentiment Lexicon was used to analyze sentiment trajectories in the collected data.

resultsA total of 12,775 valid comments were collected on Sina Weibo about public opinion related to a doctor-patient conflict. Thematic and sentiment analyses showed that the public's sentiments were highly negative during the outbreak period (disgust: 10,201/30,433, 33.52%; anger: 6792/30,433, 22.32%) then smoothly changed to positive and negative during the spread period (sorrow: 2952/8569, 34.45%; joy: 2782/8569, 32.47%) and tended to be rational and peaceful during the decline period (joy: 4757/14,543, 32.71%; sorrow: 4070/14,543, 27.99%). However, no matter how emotions changed, each period's leading tone contained many negative sentiments.

conclusionsThis study simultaneously examined the dynamics of theme change and sentiment evolution in crises involving violence against doctors. It discovered that public sentiment evolved alongside thematic changes, with the dominant negative tone from the initial stage persisting throughout. This finding, distinguished from prior research, underscores the lasting influence of early public sentiment. The results offer valuable insights for medical institutions and authorities, suggesting the need for tailored risk communication strategies responsive to the evolving themes and sentiments at different stages of a crisis.

Indexed as

PhysiciansPublic OpinionViolenceChinaEast Asian PeopleEmotionsFemaleHumansMalePhysician-Patient Relationsdoctor-patient conflictlatent Dirichlet allocationLDApublic health crisissentiment analysissocial media analysis

Identifiers

PMID40111382
PMCPMC11969123

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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.