Disentangled Graph Representation with Contrastive Learning for Rumor Detection

With many social problems nowadays, rumor detection in social media has become increasingly important. Previous works proposed classical and deep learning methods to extract information from features or rumor propagation structures. However, these methods either require lots of labeled data or are disturbed by noise nodes easily. To address these challenges, we propose a novel method that Disentangles graph representations with Contrastive learning for Rumor Detection (DCRD). Specifically, we design a graph contrastive learning strategy, significantly reducing the requirement of labeled data. We disentangle attention and redundant graph representations to extract intrinsic features and exclude the influence of redundant information. In addition, we utilize the disentangled two parts as hard negative samples to enhance contrastive learning further. Experiment results on two real-world datasets show that DCRD outperforms state-of-the-art models. More validation experiments demonstrate the data efficiency and robustness of our method.

1. INTRODUCTION

With the rapid popularity of the Internet, users are accustomed to searching for information online. However, the quality of information posted on social media varies greatly. Many users create rumors intentionally, the authenticity cannot be guaranteed. It will cause great harm to society and the economy. Therefore, effective and generalizable rumor detection methods are necessary.