Towards Robust False Information Detection on Social Networks with Contrastive Learning

Abstract

Constructing a robust conversation graph based false information detection model is crucial for real social platforms. Recently, graph neural network (GNN) methods for false information detection have achieved significant advances. However, we empirically find that slight perturbations in the conversation graph can cause the predictions of existing models to collapse. To address this problem, we present RDCL, a contrastive learning framework for false information detection on social networks, to obtain robust detection results. RDCL leverages contrastive learning to maximize the consistency between perturbed graphs from the same original graph and minimize the distance between perturbed and original graphs from the same class, forcing the model to improve resistance to data perturbations. Moreover, we prove the importance of hard positive samples for contrastive learning and propose a hard positive sample pairs generation method (HPG) for conversation graphs, which can generate stronger gradient signals to improve the contrastive learning effect and make the model more robust. Experiments on various GNN encoders and datasets show that RDCL outperforms the current state-of-the-art models.