| 重大公共卫生事件中民众诉求的主题挖掘与演变透视 |
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| 引用本文： | 杨建梁,刘越男,祁天娇,何思源.重大公共卫生事件中民众诉求的主题挖掘与演变透视[J].图书馆论坛,2021(4):121-131. |
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| 作者姓名： | 杨建梁 刘越男 祁天娇 何思源 |
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| 作者单位： | 中国人民大学信息资源管理学院 |
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| 摘 要： | 2020年初COVID-19的突发对社会生产生活造成巨大挑战,也对政府治理能力提出重大考验。疫情期间的公众舆论在充分反映民众诉求的同时,也对政府治理起到重要的推动作用。现有关于疫情期间公众舆论的研究成果,大多从舆论的价值角度分析,缺少更深层次对民众诉求的挖掘。文章基于人民网《领导留言板》数据对新冠疫情期间民众对政府的诉求进行分析,通过隐含狄利克雷分布主题模型对民众诉求的主题进行挖掘,并分析各类主题的时空演变特征,以及演变特征与防疫政策的关联。研究发现,疫情期间民众诉求的主题可以归纳为5类:社区管理、医疗防疫、学校教育、交通物流和经济措施,不同主题的热度在时间和空间上具有明显差异,这种差异与疫情发展有关,也与不同时间发布的防疫政策相关。对民众诉求主题及其与政府政策关系的研究,可为政府部门今后应对重大公共卫生事件提供借鉴。
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| 关 键 词： | 文本挖掘 主题模型 舆情分析 数字治理 |
Topic Mining and Evolution Analysis of Public Demands During Major Public Health Events |
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| YANG Jianliang,LIU Yuenan,QI Tianjiao,HE Siyuan.Topic Mining and Evolution Analysis of Public Demands During Major Public Health Events[J].Library Tribune,2021(4):121-131. |
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| Authors: | YANG Jianliang LIU Yuenan QI Tianjiao HE Siyuan |
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| Abstract: | The outbreak of COVID-19 in early 2020 brought a great challenge to both people’s everyday life and government’s governance capability.During the COVID-19 epidemic,public opinion not only reflected public demands,but also helped to promote and supervise government’s governance.However,most of current researches on public opinion during the COVID-19 epidemic are carried out from the perspective of the value of public opinion,and there is a lack of in-depth exploration of public demands.This paper focuses on the public demands during the COVID-19 epidemic,explores the topics of public demands through LDA model,and analyzes the topics’spatial-temporal evolution and their correlation with the epidemic prevention policy.The result shows that during the COVID-19 epidemic,public demands can be classified into five major topics,i.e.,community management,health and epidemic prevention,school education,transportation,and economic measures.Different topics are hotly discussed in obviously different time and different regions,and such differences are often closely related with the changes of the epidemic outbreak and the enforcement of epidemic prevention policies.Such findings could provide some reference for government’s dealing with major public health events in the future. |
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| Keywords: | text mining topic model public opinion analysis digital governance |
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