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| Topic Transition Paths and Risk Assessment of Online Public Opinion in Public Emergencies |
| Zhou Wei1,2, An Lu1,2,3, Han Ruilian2 |
1.Center for Studies of Information Resources, Wuhan University, Wuhan 430072
2.School of Information Management, Wuhan University, Wuhan 430072
3.Institute of Data Intelligence, Wuhan University, Wuhan 430072 |
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Abstract Exploring the topic transition nodes of online public opinion in public emergencies and conducting multi-phase risk assessments are of great significance for accurately addressing crises of online public opinion in these cases and providing dynamic guidance strategies. This paper proposes a method for identifying topic transition paths and conducting a multiphase risk assessment of online public opinion in emergency situations. First, a temporal semantic co-occurrence network was constructed by integrating the RoBERTa model, and network community topics were discovered using the Louvain-CFDP algorithm. Second, a model for detecting topic transitions was developed to generate topic transition paths and identify and analyze multiple types of transition paths and risk fluctuation characteristics. Taking the “Japanese nuclear contaminated water discharge into the sea” incident as an example, the empirical analysis identified three types of transition paths: event development, emotional aggregation, and derivative event types. The characteristics, risk features, and differences between the three types of paths were analyzed. The results show that the proposed method for topic transition paths and risk assessment can comprehensively demonstrate the topic transition of emergencies on social media, provide guidance and references for government departments to quickly identify high-risk topics, and formulate precise and effective public opinion risk guidance schemes.
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Received: 01 April 2024
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