Abstract: In recent years,in order to deal with various social problems which are caused by the wide spreading of rumors,researchers have developed many deep learning-based rumor detection methods.Although these methods improve detection performance by learning the high-level representation of rumor from its propagation structure,they still suffer the problem of lower reliability and cumulative errors effect,due to the ignoring of edges’ uncertainty when constructing the propagation network.To address such a problem,this paper proposes the edge-inference contrastive learning(EIC) model.EICL first constructs a propagation graph based on timestamps of retweets(comments) for a given message.Then,it augments the event propagation graph to capture the edge uncertainty of the propagation structure by a newly designed edge-weight adjustment strategy.Finally,it employs the contrastive learning technique to solve the sparsity problem of the original dataset and improve the model generalization.Experimental results show that the accuracy of EICL is improved by 2.0% and 3.0% on Twitter15 and Twitter16,respectively,compared with other state-of-the-art baselines,which demonstrate that it can significantly improve the performance of rumor detection on social media.

Key words: Rumor detection, Contrastive learning, Data augmentation, Causal inference