基于异构图注意力网络的微博谣言监测模型

Microblog rumor detection model based on heterogeneous graph attention network

社交媒体方便了人们的日常交流和信息传播,同时也是谣言滋生和传播的温床,因此如何在谣言传播早期自动监测极具现实意义,而现有的检测方法没有充分利用微博信息传播图的语义信息。为了解决这个问题,基于异构图注意力网络(HAN)构建了谣言监测模型MicroBlog-HAN。该模型采用含有节点级注意力和语义级注意力的分层注意力机制。首先,节点级注意力结合微博节点的邻居生成两组具有特定语义的节点嵌入;然后,语义级注意力融合不同语义,得到最终的节点嵌入,并输入到分类器中执行二分类任务;最后,给出输入微博是谣言还是非谣言的分类结果。在两个真实的微博谣言数据集上的实验结果表明,MicroBlog-HAN模型可以实现微博谣言较准确的识别,准确率超过87%。

Social media highly facilitates people’s daily communication and disseminating information,but it is also a breeding ground for rumors.Therefore,how to automatically monitor rumor dissemination in the early stage is of great practical significance,but the existing detection methods fail to take full advantage of the semantic information of the microblog information propagation graph.To solve this problem,based on Heterogeneous graph Attention Network(HAN),a rumor monitoring model was built,namely MicroBlog-HAN.In the model,a hierarchical attention mechanism including node-level attention and semantic-level attention was adopted.First,the neighbors of microblog nodes were combined by the node-level attention to generate two groups of node embeddings with specific semantics.After that,different semantics were fused by the semantic-level attention to obtain the final node embeddings of microblog,which were then treated as the classifier’s input to perform the binary classification task.In the end,the classification result of whether the input microblog is rumor or not was given.Experimental results on two real-world microblog rumor datasets convincingly prove that MicroBlog-HAN model can accurately identify microblog rumors with an accuracy over 87%.