• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

放松对新冠疫情的管控措施会对中国互联网用户产生影响吗?基于社交媒体的主题和情感分析。

Will the Relaxation of COVID-19 Control Measures Have an Impact on the Chinese Internet-Using Public? Social Media-Based Topic and Sentiment Analysis.

机构信息

Department of Science and Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.

出版信息

Int J Public Health. 2023 Aug 10;68:1606074. doi: 10.3389/ijph.2023.1606074. eCollection 2023.

DOI:10.3389/ijph.2023.1606074
PMID:37637486
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10448249/
Abstract

In December 2022, the Chinese government announced the further optimization of the implementation of the prevention and control measures of COVID-19. We aimed to assess internet-using public expression and sentiment toward COVID-19 in the relaxation of control measures in China. We used a user-simulation-like web crawler to collect raw data from Sina-Weibo and then processed the raw data, including the removal of punctuation, stop words, and text segmentation. After performing the above processes, we analyzed the data in two aspects. Firstly, we used the Latent Dirichlet Allocation (LDA) model to analyze the text data and extract the theme. After that, we used sentiment analysis to reveal the sentiment trend and the geographical spatial sentiment distribution. A total of five topics were extracted according to the LDA model, namely, Complete liberalization, Resource supply, Symptom, Knowledge, and Emotional Outlet. Furthermore, sentiment analysis indicates that while the percentages of positive and negative microblogs fluctuate over time, the overall quantity of positive microblogs exceeds that of negative ones. Meanwhile, the geographical dispersion of public sentiment on internet usage exhibits significant regional variations and is subject to multifarious factors such as economic conditions and demographic characteristics. In the face of the relaxation of COVID-19 control measures, although concerns arise among people, they continue to encourage and support each other.

摘要

2022 年 12 月,中国政府宣布进一步优化实施 COVID-19 防控措施。本研究旨在评估中国放松防控措施后,互联网用户对 COVID-19 的表达和情绪。我们使用用户模拟式网络爬虫从新浪微博上收集原始数据,然后对原始数据进行处理,包括去除标点符号、停用词和文本分段。完成上述步骤后,我们从两个方面分析数据。首先,我们使用潜在狄利克雷分配(LDA)模型分析文本数据并提取主题。然后,我们使用情感分析揭示情感趋势和地理空间情感分布。根据 LDA 模型共提取了五个主题,分别是完全自由化、资源供应、症状、知识和情感出口。此外,情感分析表明,虽然积极和消极微博的百分比随时间波动,但积极微博的总量超过消极微博。同时,互联网使用中的公众情绪的地理分布表现出显著的区域差异,并受到经济条件和人口特征等多种因素的影响。面对 COVID-19 防控措施的放松,尽管人们感到担忧,但他们继续相互鼓励和支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e585/10448249/f9bd5cc071e6/ijph-68-1606074-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e585/10448249/9ea574eb3d1a/ijph-68-1606074-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e585/10448249/3f26d91a5143/ijph-68-1606074-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e585/10448249/f65b1cbc9a28/ijph-68-1606074-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e585/10448249/b945c32e8261/ijph-68-1606074-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e585/10448249/f9bd5cc071e6/ijph-68-1606074-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e585/10448249/9ea574eb3d1a/ijph-68-1606074-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e585/10448249/3f26d91a5143/ijph-68-1606074-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e585/10448249/f65b1cbc9a28/ijph-68-1606074-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e585/10448249/b945c32e8261/ijph-68-1606074-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e585/10448249/f9bd5cc071e6/ijph-68-1606074-g005.jpg

相似文献

1
Will the Relaxation of COVID-19 Control Measures Have an Impact on the Chinese Internet-Using Public? Social Media-Based Topic and Sentiment Analysis.放松对新冠疫情的管控措施会对中国互联网用户产生影响吗?基于社交媒体的主题和情感分析。
Int J Public Health. 2023 Aug 10;68:1606074. doi: 10.3389/ijph.2023.1606074. eCollection 2023.
2
Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data.新冠疫情期间中国社交媒体用户表达的担忧:对新浪微博数据的内容分析
J Med Internet Res. 2020 Nov 26;22(11):e22152. doi: 10.2196/22152.
3
Sentiment Analysis of Texts on Public Health Emergencies Based on Social Media Data Mining.基于社交媒体数据挖掘的突发公共卫生事件文本情感分析。
Comput Math Methods Med. 2022 Aug 9;2022:3964473. doi: 10.1155/2022/3964473. eCollection 2022.
4
Exploring the Chinese Public's Perception of Omicron Variants on Social Media: LDA-Based Topic Modeling and Sentiment Analysis.社交媒体上公众对奥密克戎变异株的认知研究:基于 LDA 的主题建模与情感分析
Int J Environ Res Public Health. 2022 Jul 8;19(14):8377. doi: 10.3390/ijerph19148377.
5
The impact factors of social media users' forwarding behavior of COVID-19 vaccine topic: Based on empirical analysis of Chinese Weibo users.社交媒体用户转发新冠疫苗话题的影响因素:基于中国微博用户的实证分析。
Front Public Health. 2022 Sep 14;10:871722. doi: 10.3389/fpubh.2022.871722. eCollection 2022.
6
Temporal and Emotional Variations in People's Perceptions of Mass Epidemic Infectious Disease After the COVID-19 Pandemic Using Influenza A as an Example: Topic Modeling and Sentiment Analysis Based on Weibo Data.基于微博数据的主题建模和情感分析:以甲型流感为例探讨 COVID-19 大流行后人们对大规模传染病的时间和情感变化
J Med Internet Res. 2023 Nov 2;25:e49300. doi: 10.2196/49300.
7
Evolution of Public Attitudes and Opinions Regarding COVID-19 Vaccination During the Vaccine Campaign in China: Year-Long Infodemiology Study of Weibo Posts.中国疫苗接种运动期间公众对 COVID-19 疫苗接种的态度和看法的演变:对微博帖子进行的为期一年的信息流行病学研究。
J Med Internet Res. 2023 Feb 16;25:e42671. doi: 10.2196/42671.
8
Exploring Public Response to COVID-19 on Weibo with LDA Topic Modeling and Sentiment Analysis.运用LDA主题建模和情感分析探索微博上公众对新冠疫情的反应。
Data Inf Manag. 2021 Jan 1;5(1):86-99. doi: 10.2478/dim-2020-0023. Epub 2022 Mar 31.
9
How Do Chinese People View Cyberbullying? A Text Analysis Based on Social Media.中国人如何看待网络欺凌?基于社交媒体的文本分析。
Int J Environ Res Public Health. 2022 Feb 5;19(3):1822. doi: 10.3390/ijerph19031822.
10
Public Discourse and Sentiment Toward Dementia on Chinese Social Media: Machine Learning Analysis of Weibo Posts.中文社交媒体中公众对痴呆症的话语和情绪:微博帖子的机器学习分析。
J Med Internet Res. 2022 Sep 2;24(9):e39805. doi: 10.2196/39805.

引用本文的文献

1
The public's perceptions and attitudes of male nursing postgraduate professional identity on Chinese social media: Qualitative study based on machine learning.中国社交媒体上公众对男性护理研究生职业认同的认知与态度:基于机器学习的定性研究
PLoS One. 2025 Sep 4;20(9):e0331379. doi: 10.1371/journal.pone.0331379. eCollection 2025.

本文引用的文献

1
Getting COVID-19: Anticipated negative emotions are worse than experienced negative emotions.感染 COVID-19:预期的负面情绪比实际经历的负面情绪更糟糕。
Soc Sci Med. 2023 Mar;320:115723. doi: 10.1016/j.socscimed.2023.115723. Epub 2023 Jan 25.
2
China public emotion analysis under normalization of COVID-19 epidemic: Using Sina Weibo.新冠疫情常态化下的中国公众情绪分析:基于新浪微博
Front Psychol. 2023 Jan 9;13:1066628. doi: 10.3389/fpsyg.2022.1066628. eCollection 2022.
3
Refocus on Immunogenic Characteristics of Convalescent COVID-19 Challenged by Prototype SARS-CoV-2.
重新关注受原型SARS-CoV-2挑战的新冠康复者的免疫原性特征。
Vaccines (Basel). 2023 Jan 4;11(1):123. doi: 10.3390/vaccines11010123.
4
Vaccination effects on post-infection outcomes in the Omicron BA.2 outbreak in Shanghai.接种疫苗对上海奥密克戎 BA.2 感染后结局的影响。
Emerg Microbes Infect. 2023 Dec;12(1):e2169197. doi: 10.1080/22221751.2023.2169197.
5
Environmental Health Responses to COVID 19 in Western Australia: Lessons for the Future.西澳大利亚州对 COVID-19 的环境健康应对措施:未来的教训。
Int J Environ Res Public Health. 2022 Jul 31;19(15):9393. doi: 10.3390/ijerph19159393.
6
Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies.通过数据挖掘方法对网络抓取的新冠疫情推文语料库进行语义分析和主题建模
Healthcare (Basel). 2022 May 10;10(5):881. doi: 10.3390/healthcare10050881.
7
Study on the virulence evolution of SARS-CoV-2 and the trend of the epidemics of COVID-19.新型冠状病毒(SARS-CoV-2)毒力演变及新型冠状病毒肺炎(COVID-19)疫情趋势研究。
Math Methods Appl Sci. 2022 Jul 30;45(11):6515-6534. doi: 10.1002/mma.8184. Epub 2022 Feb 24.
8
JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook.《JUE洞察:新冠病毒病的地理传播与通过脸书衡量的社交网络结构相关》
J Urban Econ. 2022 Jan;127:103314. doi: 10.1016/j.jue.2020.103314. Epub 2021 Jan 9.
9
Public Trust in COVID-19 Prevention and Responses Between January and May 2020 in Bangladesh.2020年1月至5月期间孟加拉国公众对新冠疫情防控与应对措施的信任度
Risk Manag Healthc Policy. 2021 Nov 1;14:4425-4437. doi: 10.2147/RMHP.S327881. eCollection 2021.
10
Nordic responses to Covid-19: Governance and policy measures in the early phases of the pandemic.北欧国家应对新冠疫情的措施:大流行早期的治理和政策措施。
Health Policy. 2022 May;126(5):418-426. doi: 10.1016/j.healthpol.2021.08.011. Epub 2021 Sep 5.