公众对暴力侵害医生行为的态度:中国用户的情绪分析。

IF 3.1 3区 医学 Q2 MEDICAL INFORMATICS
Yuwen Zheng, Meirong Tian, Jingjing Chen, Lei Zhang, Jia Gao, Xiang Li, Jin Wen, Xing Qu
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引用次数: 0

摘要

背景:在网络和现实世界中,针对医生的暴力行为都会引起公众的关注。了解公众情绪在此类危机中是如何演变的,对于制定管理情绪和重建信任的策略至关重要:本研究旨在根据舆论生命周期理论量化公众情绪的差异,并描述在中国发生的备受关注的暴力伤医危机中公众情绪是如何演变的:本研究使用词频-反向文档频率(TF-IDF)算法从文本评论中提取关键术语并创建关键词云。采用潜在狄利克特分配(LDA)主题模型分析公众情绪的主题趋势和变化。集成的中文情感词典用于分析收集数据中的情感轨迹:结果:在新浪微博上共收集到 12,775 条与医患冲突相关的有效舆论评论。主题分析和情感分析表明,在医患冲突爆发期间,公众的情绪高度负面(厌恶:10201/30433,33.52%;愤怒:6792/30433,22.32%),然后平稳地转变为积极。32%),然后在蔓延期平稳地转变为积极和消极(悲伤:2952/8569,34.45%;喜悦:2782/8569,32.47%),并在衰退期趋于理性和平和(喜悦:4757/14543,32.71%;悲伤:4070/14543,27.99%)。然而,无论情绪如何变化,每个时期的主导基调都包含了许多负面情绪:本研究同时考察了暴力伤医危机中主题变化和情绪演变的动态过程。研究发现,公众情绪随着主题的变化而变化,最初阶段的负面基调始终占据主导地位。这一发现有别于以往的研究,强调了早期公众情绪的持久影响。研究结果为医疗机构和当局提供了宝贵的启示,表明有必要根据危机不同阶段不断变化的主题和情绪,制定有针对性的风险沟通策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Public Attitudes Toward Violence Against Doctors: Sentiment Analysis of Chinese Users.

Background: Violence 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.

Objective: This 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.

Methods: This 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.

Results: A 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.

Conclusions: This 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.

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来源期刊
JMIR Medical Informatics
JMIR Medical Informatics Medicine-Health Informatics
CiteScore
7.90
自引率
3.10%
发文量
173
审稿时长
12 weeks
期刊介绍: JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals. Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.
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