Self-Supervised Rumor Detection with Augmented Variational Graphs

Detecting rumors on social media has grown in importance as the amount of digital material available online grows quickly. Recent approaches to rumor detection heavily rely on supervised learning, which requires a significant amount of labeled data for training and provides limited interpretability of prediction results. Furthermore, these approaches show a lack of robustness and are vulnerable to overfitting. In this paper, we propose a novel framework Self-Supervised Rumor Detection with Augmented Variational Graphs (SSRD-AVG) from a self-supervised learning (SSL) view, which employs a pre-trained generative model to facilitate data augmentation with enhanced interpretability. Specifically, the generative model first harnesses neighboring information to extract salient features for rumor propagation structures and user engagement. Then, the obtained augmented features are subsequently utilized for self-supervised learning. Finally, we fine-tuned the Graph Neural Network (GNN) with labeled data for rumor detection. Comprehensive experiments demonstrate that our model attains state-of-the-art performance and remains robust in real-world scenarios.