陈俊帆
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陈俊帆
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论文
Towards Robust False Information Detection on Social Networks with Contrastive Learning
发布时间：2025-10-22点击次数：
发表刊物：
Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM), CCF-B
摘要：
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.
合写作者：
Guanghui Ma,胡春明, Ling Ge,陈俊帆, Hong Zhang,张日崇
论文类型：
国际学术会议
页面范围：
1441-1450
是否译文：
否
发表时间：
2022-01-01