基于图卷积网络的多特征融合谣言检测方法OA北大核心CSTPCD

At present,most rumor detection work mainly based on the original text content,communication struc-ture and communication text content of Twitter or Weibo.However,these methods ignored the effective integration of original text features with other features,as well as the role of propagating users in the process of rumor propaga-tion.Aiming at the shortcomings of the existing work,a multi-feature fusion model GCNs-BERT based on graph convolutional network was proposed,which combined the features of the original text,the propagating user and the propagating structure.Firstly,a propagation graph was constructed based on the propagation structure and the prop-agation users,and the combination of multiple user attributes was used as the propagation node feature.Then,mul-tiple graph convolutional networks were used to learn the representation of the propagation graph with different user attribute combinations,and BERT model was used to learn the feature representation of the original text content.Finally,the fusion with the features learned by the graph convolutional network was used to detect rumors.A large number of experiments using publicly available Weibo data sets showed that the GCNs-BERT model was significant-ly better than the baseline method.In addition,the generalization ability experiment of GCNs-BERT model was conducted on the novel coronavirus epidemic data set.The training sample size of this data set was only 1/5 of that of the public Weibo data set,and the accuracy rate was still 92.5%,which proved that the model had good gener-alization ability.