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重大突发公共卫生事件应对政策调整前后网络舆情的演变

How online public opinion evolves before and after policy adjustments in response to major public health emergencies.

作者信息

Niu Zhendong, Gao Yunyun, Wu Xusheng, Hu Qingyuan, Hu Dehua

机构信息

Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha, China.

School of Information Management, Sun Yat-sen University, Guangzhou, China.

出版信息

Front Public Health. 2025 Jun 9;13:1438854. doi: 10.3389/fpubh.2025.1438854. eCollection 2025.


DOI:10.3389/fpubh.2025.1438854
PMID:40552227
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12183273/
Abstract

BACKGROUND: In recent years, incidents of public opinion triggered by major public health emergencies have emerged endlessly. Existing studies have focused on public attitudes during the early stages of containment measures but lacked research on how public opinion evolves after those measures are relaxed. In late 2022, however, China optimized its COVID-19 control measures, providing a unique window for this study. OBJECTIVE: To reveal public attitudes toward the adjustment of response measures for major public health emergencies and how these attitudes evolve over time, and to provide a reference for improving related policies and managing public opinion. METHODS: We collected Baidu Index and Weibo post data related to "epidemic prevention and control" between October 11, 2022 and March 15, 2023. Guided by the "Public Opinion Life Cycle Theory," we analyzed the evolution of public opinion intensity using the Baidu Index. We applied the SKEP model for sentiment analysis on Weibo posts, exploring changes in public sentiment and differences among groups. Additionally, we used the LDA model for topic mining on Weibo posts, examining the evolution of discussion topics and their underlying causes. RESULTS: During the early stages of adjustments to prevention and control measures, public opinion surged but quickly subsided to a level significantly lower than before, following the announcement of more targeted measures. In the long term, the public generally holds a positive attitude toward these adjustments, though negative sentiment may emerge in the short term. Prior to the adjustments, discussions focused on community prevention and control. In the early phase, debates were intense, with expectations for a return to normal life and economic recovery alongside concerns about health risks and medical resources. After a prolonged adjustment period, discussions on economic and daily-life topics increased, but concerns about medication and reinfection risks remained high. CONCLUSION: To guide the healthy development of public opinion, policymakers should clearly explain the rationale for policy adjustments, promptly address public concerns, and encourage enterprises and opinion leaders to share positive information; additionally, they should ensure sufficient medical resources are secured before implementing policy changes and roll them out in a well-organized, step-by-step manner.

摘要

背景:近年来,重大突发公共卫生事件引发的舆情事件层出不穷。现有研究多聚焦于防控措施初期公众的态度,而对措施放松后舆情如何演变缺乏研究。然而,2022年末中国优化了新冠疫情防控措施,为该研究提供了独特契机。 目的:揭示公众对重大突发公共卫生事件应对措施调整的态度及其随时间的演变情况,为完善相关政策和舆情管理提供参考。 方法:收集2022年10月11日至2023年3月15日期间与“疫情防控”相关的百度指数和微博帖子数据。以“舆情生命周期理论”为指导,利用百度指数分析舆情强度的演变。应用SKEP模型对微博帖子进行情感分析,探究公众情绪变化及群体差异。此外,使用LDA模型对微博帖子进行主题挖掘,考察讨论主题的演变及其背后原因。 结果:在防控措施调整初期,舆情激增,但随着更具针对性的措施出台,迅速回落至显著低于之前的水平。从长期来看,公众对这些调整总体持积极态度,不过短期内可能会出现负面情绪。调整前,讨论集中在社区防控。初期,争论激烈,人们期望恢复正常生活和经济复苏,同时担心健康风险和医疗资源。经过较长的调整期后,关于经济和日常生活话题的讨论增加,但对用药和再感染风险的担忧仍然很高。 结论:为引导舆情健康发展,政策制定者应清晰阐释政策调整的理由,及时回应公众关切,鼓励企业和意见领袖分享正面信息;此外,在实施政策变更前应确保有足够的医疗资源,并有序、逐步地推出政策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/35ca6a6de048/fpubh-13-1438854-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/491cfd358219/fpubh-13-1438854-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/dca4e0a031fd/fpubh-13-1438854-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/151325d3fdb1/fpubh-13-1438854-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/3331acb9117d/fpubh-13-1438854-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/693c5c2aa631/fpubh-13-1438854-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/483b3c19f8f9/fpubh-13-1438854-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/2d052c6029e7/fpubh-13-1438854-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/66565752ca1b/fpubh-13-1438854-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/35ca6a6de048/fpubh-13-1438854-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/491cfd358219/fpubh-13-1438854-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/dca4e0a031fd/fpubh-13-1438854-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/151325d3fdb1/fpubh-13-1438854-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/3331acb9117d/fpubh-13-1438854-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/693c5c2aa631/fpubh-13-1438854-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/483b3c19f8f9/fpubh-13-1438854-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/2d052c6029e7/fpubh-13-1438854-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/66565752ca1b/fpubh-13-1438854-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37d/12183273/35ca6a6de048/fpubh-13-1438854-g009.jpg

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本文引用的文献

[1]
Exploring the Lagged Correlation Between Baidu Index and Influenza-Like Illness - China, 2014-2019.

China CDC Wkly. 2024-6-28

[2]
Recommended Strategies to Prioritize Evidence-Based Public Health Authority.

J Public Health Manag Pract. 2024

[3]
Tracking Chinese Online Activity and Interest in Osteoporosis Using the Baidu Index.

Cureus. 2024-4-5

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Public Opinion About COVID-19 on a Microblog Platform in China: Topic Modeling and Multidimensional Sentiment Analysis of Social Media.

J Med Internet Res. 2024-1-31

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Twitter sentiment analysis: An Arabic text mining approach based on COVID-19.

Front Public Health. 2022

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