Graph Convolution Spatio-Temporal Attention Fusion and Graph Reconstruction Method for Rumor Detection
The rapid development of the Internet has brought convenience to people's social life,but it also creates conditions for the generation and spread of rumors.The fast propagation speed and bad impact of rumors have attracted wide social attention.However,in complex social networks,the dynamic change of rumor propagation state,the existence of interference information in the propagation process,and the uncertainty of propagation all bring difficulties to rumor detection.In order to solve the above problems,this study proposes a graph convolution spatio-temporal attention fusion and graph reconstruction method(STAFRGCN)for rumor detection,and all the speeches to be detected are detected twice to reduce the probability of misjudg-ment.Firstly,a temporal progressive convolution module(TPC)is used to integrate the propagation status information of the speeches to be detected in the time dimension.Then,attention is used to extract and fuse the main propagation feature information in two aspects of time and space respectively,and the fusion result is used for the first rumor detection.After that,the total graph structure of the detected speech propagation is adjusted based on long short-term memory(LSTM)prediction and graph recon-struction method.It is combined with the first detection results for the second detection.Experiments show that the detection ac-curacy of STAFRGCN on Twitter15,Twitter16 and Weibo datasets is 92.2％,91.8％and 96.5％,respectively.Compared with SOTA model(KAGN),the accuracy is increased by 3.0％,1.5％and 1.4％on the 3 datasets,respectively.
万方数据
维普
