[Objective] This study reveals patterns and evolution of public opinion on Weibo during natural disasters from the perspectives of trending topics and information dissemination. [Methods] We proposed a machine learning approach to extract the valid data of natural disasters from Weibo. Then, we employed a deep learning model to cluster these textual posts. Finally, we investigated the information dissemination patterns with complex network analysis. [Results] The accuracy of our extractor for valid disaster information reached 0.82. The clusters of textual posts indicated the changes of trending topics. The structure of information dissemination during disasters was sparse. The sizes of online communities expanded constantly while their distribution unchanged. Users in different regions had different preferences for information sources. [Limitations] We did not conduct experiment to examine data from different social platforms. [Conclusions] The proposed method could effectively identify public opinion events during natural disasters.
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Ma Yingxue,Zhao Jichang. Patterns and Evolution of Public Opinion on Weibo During Natural Disasters： Case Study of Typhoons and Rainstorms. Data Analysis and Knowledge Discovery, 2021, 5(6): 66-79.
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