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基于在线媒体的新冠疫情社会舆情多视角分析

黄晓辉1,2,卢焱1,唐锡晋1,2   

  1. 1. 中国科学院数学与系统科学研究院,  北京 100190;2. 中国科 学院大学, 北京 100049
  • 出版日期:2021-08-25 发布日期:2021-11-23

黄晓辉, 卢焱, 唐锡晋. 基于在线媒体的新冠疫情社会舆情多视角分析[J]. 系统科学与数学, 2021, 41(8): 2182-2198.

HUANG Xiaohui, LU Yan, TANG Xijin. Multi-Perspective Analysis of Public Opinion Related to COVID-19 Based on Online Media[J]. Journal of Systems Science and Mathematical Sciences, 2021, 41(8): 2182-2198.

Multi-Perspective Analysis of Public Opinion Related to COVID-19 Based on Online Media

HUANG Xiaohui1,2, LU Yan1, TANG Xijin1,2   

  1. 1. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190; 2. University of Chinese Academy of Sciences, Beijing 100190
  • Online:2021-08-25 Published:2021-11-23
2020年初爆发的新冠肺炎疫情是近年来最大公共卫生事件, 挖掘 期间关于医疗物资社会舆情的性质和演变趋势, 有助于我们了解事件演化和社 会应对机制.文章从话题演化视角和事件结构视角展开研究.文章首先基于``口罩''相关的新 闻语料库, 利用LDA主题模型挖掘疫情期间不同时间窗口下的新闻热点 话题内容, 分析主要话题社会疫情、社会防疫、口罩生产与监管和口罩进出 口的演化关系和演化路径.针对于口罩进出口中的海关查获事件, 文章 依据事件中关键词构建共现关键词网络, 获悉查获事件中的主要四 种类型, 并通过构建海关-关键词二模网得到海关与查获事件关键词的关系, 揭示 查获事件类型在对应海关的分布.
COVID-19 which outbroke in early 2020, is the most significant public health emergency in recent years. Excavating the characteristic and evolution tendency of social public opinion about medical supplies during the epidemic period will help us understand the evolution of this emergency and the social response mechanism. This study conducts research from the perspective of topic evolution and event structure. Based on the ``mask" related news corpus, this study uses the LDA topic model to dig some hot topics from the news under different periods during the epidemic. Therefore, the relationship and evolution path of some main topics, which are about the social epidemic situation, social epidemic prevention, mask production and supervision, and the import and export of mask, are analyzed. In view of the customs seizures in the import and export of masks, this study builds a co-occurrence keywords network based on the keywords in the events, learns the main four types of seizure events, and obtains the relationship between the customs and the seizure event keywords through the construction of the bipartite networks of custom-keywords.
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