融合异质子图序列的社交网络假消息检测方法OA

With the rapid development of social media,fake news in social networks has increased massively,which has a bad impact on society,so fake news detection is one of the key problems that need to be solved in social networks.In order to solve the above problem,a graph convolutional neural network model based on heterogeneous graph subgraph sequences is proposed.The model has three stages.The first stage is to analyze the text attributes and propagation paths of tweets on the collected tweet cluster dataset,and use natural language processing,propagation graph construction and other means to process tweets.In the second stage,a user-tweet-word heterogeneous map and a tweet propagation network are established,and the above graphs are sampled based on random walks,and subgraph sequences are generated to represent the original graphs.Finally,the subgraph sequence is used to train the graph convolutional neural network.Experimental results demonstrate that the proposed model effectively addresses the feature sparsity caused by varying propagation scales.On public datasets,it achieves a detection accuracy of 92.43%,outperforming the Fake News Graph Framework(FANG)framework by 13.27%.The recall and F1-score are improved by 14.81%and 15.23%,respectively.