| 杨海林,田军,李莉芳.自然灾害事件主题发现与演化研究——以知乎自然灾害讨论为例[J].情报工程,2023,9(4):040-053 |
| 自然灾害事件主题发现与演化研究——以知乎自然灾害讨论为例 |
| A Study on the Topic Detection and Evolution Analysis of Natural Disaster Event: Taking the Natural Disaster Discussion in Zhihu as an Example |
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| DOI：10.3772/j.issn.2095-915X.2023.04.004 |
| 中文关键词: 自然灾害事件；主题演化；知乎；主题模型；舆情传播 |
| 英文关键词: Natural disaster events; Topic evolution; Zhihu; Topic model; Public opinion dissemination |
| 基金项目:国家自然科学基金资助项目“政府应急管理能力成熟度评估研究”(71171157)。 |
| 作者 | 单位 | | 杨海林 | 1. 西安交通大学管理学院 西安 710049； | | 田军 | 1. 西安交通大学管理学院 西安 710049； | | 李莉芳 | 2. 伦敦国王学院生物统计与健康信息学系 伦敦 SE 8AF |
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| 中文摘要: |
| [目的/意义]我国是自然灾害最为频发的国家之一，网络上的灾害讨论信息容易引发负面舆情影响，危害社会稳定，研究自然灾害事件的舆情主题演化规律能协助政府制定舆论管理策略。[方法/过程]本文以两次暴雨洪涝灾害“2021 年 7·20 河南暴雨”及“2020 年中国南方洪涝灾害”事件为背景，采用 Correlated Topic Model (CTM) 主题模型分析方法对知乎用户所发布信息进行建模分析。具体而言，本文结合主题强度和主题传播广度两方面分析用户在暴雨灾害中的注意力转变，并深入探究了用户参与情况对主题传播的影响，进而推演灾害期间主题传播内容与演化规律。[结果/结论]研究得出了两次自然灾害事件的主题传播内容与演化过程情况，为未来自然灾害事件中政府的舆情应对和处理提供了参考。 |
| 英文摘要: |
| [Objective/Significance] Natural disasters such as rainstorms and flooding happen frequently in China. During such disasters, public opinions disseminating quickly on social media which may trigger negative effects on social stable. Extracting the discussion themes of the public during natural disaster can help the government agencies formulate efficient disaster management strategies. [Methods/Processes] The study applied one rainstorm ("July 20, 2021 rainstorm in Henan Province") and one flooding ("2020 flood in Southern China") Zhihu datasets to investigate the main topics. Specifically, Correlated Topic Model (CTM) was applied to examine the topics in the posts of Zhihu users. Specifically, this study combines the two aspects of topic intensity and topic communication breadth to analyze users' attention changes during rainstorms, and explores the impact of user participation on topic communication, and then deduce the content and evolution patterns of topic communication during
disasters. [Results/Conclusions] We uncovered the topic content and evolution patterns of topic communication during disasters and provided practical insights to the government agencies. |
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