Rumor detection method based on breadth-depth sampling and graph convolutional networks
A new detection method was proposed to resolve the problems of early data loss and insufficient feature utilization in the field of rumor detection.In order to fully extract early propagation features of events,a breadth sampling method was proposed,and propagation sequences corresponding to events were constructed.A Transformer was utilized to explore semantic correlations between long-distance comments and to construct propagation sequence features for events.In order to effectively uncover the structural features of event propagation,a depth sampling method based on path length was proposed,and information propagation subgraphs and information aggregation subgraphs corresponding to events were constructed.The advantage of graph convolutional networks in exploring graph structural features was leveraged to obtain the propagation structure features corresponding to events.Feature representation of the propagation sequence and propagation structure for events were concatenated to obtain the ultimate feature representation.Validation experiments for the proposed method were conducted on two public datasets(Weibo2016 and CED).Results show that the proposed method is generally superior to existing typical methods.Compared to baseline methods,the proposed method has significant improvements in accuracy and F1 score,validating the effectiveness of the method in the field of rumor detection.
万方数据
维普
