Lgt: long-range graph transformer for early rumor detection

Society is now situated in an epoch where the creation and spread of fake news have become remarkably effortless. Hence, conducting early rumor detection tasks is imperative. To handle this task, a key ideal is to model the interactive information between users who spread the news. To this end, existing methods usually use multiple stacked GNN layers to capture long-range user information. However, recent work has shown that traditional GNNs may struggle to capture important information when dealing with k-hop neighbors of users, thus hurting the performance of models. To address this problem, we propose a Long-range Graph Transformer for early rumor detection (LGT), which uses transformers to capture long-range dependencies between users. First, we use a graph convolutional attentive network to extract the publishing features. Second, we combine graph neural network and transformer to capture the long-range interaction features of users. Then, we employ the convolutional neural network to extract the text features and use the attention mechanism to fuse with the interactive information to obtain the aggregated interaction features. In addition, we collect the user's credibility score as additional information. Finally, the above three features are fused to generate a new representation. Extensive experiments using three authentic datasets demonstrate that, in comparison to the baseline, LGT has achieved significant improvement. It effectively identifies rumors quickly while maintaining an accuracy rate exceeding 94%.