Contrastive Learning with Edge‐Wise Augmentation for Rumor Detection

Abstract

Exploring and modeling the spreading process of rumors have shown great potential in improving rumor detection performance. However, existing propagation‐based rumor detection models often overlook the uncertainty of the underlying propagation structure and typically require a large amount of labeled data for training. To address these challenges, we propose a novel rumor detection framework, namely, the Uncertainty‐Inference Contrastive Learning (UICL) model. Specifically, UICL innovatively incorporates an edge‐wise augmentation strategy into the general contrastive learning framework, including an edge‐inference augmentation component and an EdgeDrop augmentation component, which primarily aim to capture the edge uncertainty of the propagation structure and alleviate the sparsity problem of the original dataset. A new negative sampling strategy is also introduced to enhance contrastive learning on rumor propagation graphs. Furthermore, we use labeled data to fine‐tune the detection module. Our experiments, conducted on three real‐world datasets, demonstrate that UICL can not only significantly improve detection accuracy but also reduce the dependency on labeled data compared to state‐of‐the‐art baselines.