王艳东, 李萌萌, 付小康, 邵世维, 刘辉. 基于社交媒体共词网络的灾情发展态势探测方法[J]. 武汉大学学报 ( 信息科学版), 2020, 45(5): 691-698, 735. DOI: 10.13203/j.whugis20190054
引用本文: 王艳东, 李萌萌, 付小康, 邵世维, 刘辉. 基于社交媒体共词网络的灾情发展态势探测方法[J]. 武汉大学学报 ( 信息科学版), 2020, 45(5): 691-698, 735. DOI: 10.13203/j.whugis20190054
WANG Yandong, LI Mengmeng, FU Xiaokang, SHAO Shiwei, LIU Hui. A New Method to Detect the Development Situation of Disasters Based on Social Media Co-word Network[J]. Geomatics and Information Science of Wuhan University, 2020, 45(5): 691-698, 735. DOI: 10.13203/j.whugis20190054
Citation: WANG Yandong, LI Mengmeng, FU Xiaokang, SHAO Shiwei, LIU Hui. A New Method to Detect the Development Situation of Disasters Based on Social Media Co-word Network[J]. Geomatics and Information Science of Wuhan University, 2020, 45(5): 691-698, 735. DOI: 10.13203/j.whugis20190054

基于社交媒体共词网络的灾情发展态势探测方法

A New Method to Detect the Development Situation of Disasters Based on Social Media Co-word Network

  • 摘要: 对灾害发生过程中产生的社交媒体数据进行主题演化探测和分析可以反映灾情的发展态势。提出了一种基于共词网络社区演化进行灾情发展态势感知的方法,首先依据词频-逆文档频率方法筛选出与主题相关的关键词汇,基于关键词的共现关系,构建以关键词为节点的社交媒体共词网络,结合模块度最优化思想,对社交媒体共词网络进行主题社区探测;然后在验证主题探测的基础上,基于时间窗口划分,对相邻时间窗口的主题社区进行演化类型判别,进而得到主题社区演化的结果;最后以2012年“7.21北京特大暴雨”灾害事件为例,利用该方法对收集到的含关键词的微博数据进行主题演化分析。实验结果表明,该方法能够很好地反映主题的演化过程,并能进一步揭示灾情的发展态势,帮助应急管理者了解灾害的发展过程,从而辅助管理者在合适的时间采取相应的应急响应措施。

     

    Abstract: The development trend of disasters can be perceived through mining and analyzing the topics evolution of social media data in disasters. A method of studying the evolution of the topic communities based on the common word network is proposed, so the development trend of the disaster situation can be sensed. Firstly, according to the word frequency-inverse document frequency analysis, the key words related to the topics are selected and a social media co-word network with keywords as nodes is constructed. Thus, the topic community detection is performed on the social media co-word network based on the module optimization. Secondly, on the basis of verifying the topic detection, and the time window division, the evolution types of the topic communities in adjacent time windows are distinguished, and then the result of the topic community evolution is obtained. Finally, taking the 7.21 Beijing Heavy Rainstorm disaster event in 2012 as an example, the proposed method is used to analyze the collected microblog data. The experiment shows that the method can reflect the evolution process of the topics well. It can further reveal the development trend of the disaster, and help emergency managers understand the development process of disasters, so as to assist managers to take appropriate emergency response measures in appropriate time.

     

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