一种融合知识图谱的图注意力神经网络谣言实时检测方法

A Real-Time Rumor Detection Method Based on the Graph Attention Neural Network Integrated with the Knowledge Graph

【目的】提高社交媒体中谣言实时检测的准确率,降低谣言传播危害。【方法】提出一种融合知识图谱的图注意力神经网络谣言实时检测方法。首先,通过知识蒸馏从外部知识图谱中获取文本内容的背景知识;其次,通过点互信息把文本和背景知识转化为加权图结构表示,利用一种考虑边权重的图注意力神经网络从加权图中学习文本的非连续语义特征;然后,通过预训练语言模型BERT学习文本的连续语义特征,利用嵌入方法把用户和内容统计特征转化为连续向量表示;最后,融合所有特征,输入全连接神经网络中进行谣言检测。【结果】在两个公开的社交媒体谣言数据集PHEME和WEIBO上的实验结果表明,所提方法的准确率分别达到了92.1%和84.0%,优于对比基线方法。【局限】所提方法没有融合帖子中可能附加的图片或视频信息,不能进行多模态融合的谣言检测。【结论】融合背景知识可以补充短文本的语义表示,融合用户和内容统计特征可以辅助文本语义特征作决策,提高检测的准确率。

[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.