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| Construction of a Multilevel Public Opinion Risk Identification Network Model Integrating Related Event Traceability |
| Liu Yizhou, Huang Wei |
| School of Business and Management, Jilin University, Changchun 130012 |
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Abstract The identification of online public opinion risks is a prerequisite for the targeted control of public opinion and is of great significance for online public opinion risk assessment and precise guidance. This study constructs a multilevel public opinion risk identification model that integrates related event tracing (multilevel risk identification model integrating related event tracing, ML-RIM-RET) at three levels: event relationship, data content, and user behavior. The model embeds a knowledge enhancement module in the event relationship layer and detaches primary risk sources by building an event graph. In the data content and user behavior layers, the model combines sentiment calculation, thematic risk analysis, and Gibbs sampling to calculate and identify public opinion risks under different related event tracing links. The empirical results indicate a long-tail effect between risk-associated event clusters and causal emergence density, indicating that a small number of risk sources have a high causal emergence density. According to the comparative experimental results, ML-RIM-RET performed the best in public opinion risk-identification in the context of enhanced knowledge of events. The model proposed in this study can quickly identify risk sources at different levels, which helps broaden the perspective of network public opinion risk governance and spatial restoration.
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Received: 17 February 2025
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