[Purpose/Significance] In public health emergencies, the rapid spread of rumors may cause mass anxiety and panic. This study aims to detect potential rumor spreaders in social media, explore and evaluate the important characteristics affecting rumor spreader identification, and provide strategies for public opinion control and network governance. [Method/Process] This study proposed a detection model for potential rumor spreaders based on multi-feature fusion in the context of public health emergencies. Firstly, the semantic features of Weibo were extracted by the BERT-BiLSTM model, and then fused with user features, Weibo features and emotion features. Finally, the user classification model was constructed based on LightGBM algorithm, and the model was explained by SHAP value. [Result/Conclusion] The experimental results show that the accuracy rate of the fusion multi-feature rumor spreader identification model for public health emergencies can reach 87.94% on the Weibo data set, indicating that the model has good detection effect. Moreover, the features of four dimensional proposed in this paper contribute to rumor spreader identification, and the text semantic features have the highest improvement in the accuracy of rumor spreader identification.
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