关键词: 公共卫生事件, 谣言识别, 微博, 多特征融合

Abstract: [Purpose/significance] In public health emergencies such as the COVID-19 epidemic, a large number of statements about the epidemic have quickly been generated on social media on the Internet, including many rumors that endanger public mental health and affect the implementation of national policies. Detecting these remarks and identifying the rumors can enable the people to respond to public health emergencies correctly, and play a positive role in maintaining social stability and network governance.[Method/process] Firstly, the confirmed rumors during the epidemic were collected for in-depth analysis, and the main features of the rumor text were extracted, including context features, topic category features, sentiment level features, keyword features, etc.; then aiming at the problem that the text feature expression in the text classification model was relatively single, different models were used to vectorize the extracted rumor text features, and then a rumor recognition model based on multi-feature fusion was constructed. In the construction of this model, TF-IDF was used to strengthen the word vector, so that the word vector can merge the keyword feature information of the word granularity while capturing the context feature. Finally, this paper used the BiLSTM+DNN model to classify the fused feature vectors.[Result/conclusion] The experimental results show that features such as topic category and emotional level all contribute to the recognition of rumors, especially the fusion of the strengthened word vector and other features to significantly improve the recognition accuracy, recall rate, F1 measure, etc. The indicators all reached more than 90%, and the effect surpassed other rumor recognition models, indicating that the method constructed in this article can respond well to the task of rumor recognition in the context of public health emergencies.

Key words: public health emergencies, rumor recognition, Weibo, multi-feature fusion