Joint Adversarial and Contrastive Graph Attention Framework for Enhanced Rumor Detection

Abstract

In recent years, due to the massive amount of information on the web, the rumor detection on social media presents a significant challenge owing to online content’s noisy, dynamic, and often adversarial nature. This work introduces a model that leverages Graph Attention Networks (GAT) enhanced with adversarial and contrastive learning to improve rumor classification performance. Experimental results on the X Dataset (formerly Twitter) demonstrate that our integrated GAT+ADV+CL model achieves satisfactory performance across multiple classification evaluation metrics, while maintaining a relatively simple architecture compared to other recent complex graph-based approaches for rumor detection. These findings highlight the effectiveness of combining robustness and representation learning in tackling the challenge of misinformation detection. © The Author(s) 2025.