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<p>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.</p>

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Joint Adversarial and Contrastive Graph Attention Framework for Enhanced Rumor Detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月16日

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  • Chintoo Kumar,
  • C. Ravindranath Chowdary,
  • Snehal Kumar Singh,
  • Muskan Nareliya,
  • Sunil Kumar Patel

摘要

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.