A Real-Time Rumor Detection Method Based on the Graph Attention Neural Network Integrated with the Knowledge Graph
[Objective]This paper aims to improve the accuracy of real-time rumor detection in social media and reduce the harm caused by rumors.[Methods]A real-time rumor detection method based on the graph attention neural network integrated with the knowledge graph is proposed.First,the background knowledge of the text is obtained from the external knowledge graph by knowledge distillation.Second,we transformed the text and background knowledge into a weighted graph structure representation by point mutual information,and a weighted graph attention neural network is used to learn the discontinuous semantic features of the text from the weighted graph.Then,the continuous semantic features of the text are learned by the pre-trained language model BERT,and the statistical features of users and content are converted into continuous vector representations using the embedding method.Finally,all the features are fused and input into the fully connected neural network for rumor detection.[Results]Experimental results on two public social media rumor datasets,PHEME and WEIBO,show that the method's accuracy reaches 92.1％and 84.0％,respectively,higher than the state-of-the-art baseline methods.[Limitations]The method does not fuse the image or video information that may be attached to the post and cannot perform multi-modal fusion rumor detection.[Conclusions]Fusion of background knowledge can complement the semantic representation of short texts.Fusion of user and content statistical features can support semantic features in decision making and improve the accuracy of the model.
Rumor Real-Time DetectionGraph Attention Neural NetworkKnowledge GraphSemantic FeaturesStatistical FeaturesUser Features
万方数据
维普
