Graph Contrastive ATtention Network for Rumor Detection

Shaohua Li, Weimin Li, Alex Munyole Luvembe, Weiqin Tong

Abstract: Detecting rumors from the vast amount of information in online social media has become a formidable challenge. Rumor detection based on rumor propagation trees benefits from crowd wisdom and has become an important research method for rumor detection. However, node representations in such methods rely on limited label information and lose a lot of node information when obtaining graph-level representations through pooling. This paper proposes a novel rumor detection model called Graph Contrastive ATtention Network (GCAT). GCAT adopts a graph attention model as the encoder, applies graph self-supervised learning without negative label pairs as an auxiliary task to update network parameters, and combines multiple pooling techniques to obtain the graph-level representation of the rumor propagation tree. To verify the effectiveness of our model, we conduct experiments on two real-world datasets. The GCAT model outperforms the optimal baseline algorithms on both datasets, proving the effectiveness of the proposed model.