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Rumor detection has become an emerging and active research field in recentyears. At the core is to model the rumor characteristics inherent in richinformation, such as propagation patterns in social network and semanticpatterns in post content, and differentiate them from the truth. However,existing works on rumor detection fall short in modeling heterogeneousinformation, either using one single information source only (e.g. socialnetwork, or post content) or ignoring the relations among multiple sources(e.g. fusing social and content features via simple concatenation). Therefore,they possibly have drawbacks in comprehensively understanding the rumors, anddetecting them accurately. In this work, we explore contrastive self-supervisedlearning on heterogeneous information sources, so as to reveal their relationsand characterize rumors better. Technically, we supplement the main supervisedtask of detection with an auxiliary self-supervised task, which enriches postrepresentations via post self-discrimination. Specifically, given twoheterogeneous views of a post (i.e. representations encoding social patternsand semantic patterns), the discrimination is done by maximizing the mutualinformation between different views of the same post compared to that of otherposts. We devise cluster-wise and instance-wise approaches to generate theviews and conduct the discrimination, considering different relations ofinformation sources. We term this framework as Self-supervised Rumor Detection(SRD). Extensive experiments on three real-world datasets validate theeffectiveness of SRD for automatic rumor detection on social media.

Rumor Detection with Self-supervised Learning on Texts and Social Graph