A Multi-modal Rumor Detection Model Based on Temporal Graph Attention Network

Shiming Li ()
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Shiming Li: Nanjing University of Aeronautics and Astronautics

A chapter in Proceedings of the 2025 10th International Conference on Social Sciences and Economic Development (ICSSED 2025), 2025, pp 890-905 from Springer

Abstract: Abstract Objective: To address the issue of insufficient mining of structural and temporal sequence features of information dissemination in existing rumor detection methods, a multi-modal rumor detection model based on temporal graph attention is designed. Methods: For the text modality, a RoBERTa pre-trained model is used as the basis, and GAT and GRU modules are introduced to extract and fuse mixed features of text and dissemination structure. For the image modality, ViT is used to extract image features. Multi-modal features are fused through self-attention and cross-attention mechanisms to complete rumor detection. Results: The accuracy and F1 value of the proposed model on the Twitter dataset reach 91.1% and 91.4%, respectively, achieving the best performance in the comparative experiments. Limitations: The performance of the model on other datasets has not been tested. Conclusion: The proposed model can effectively improve the rumor detection effect of multi-modal posts on social media.

Keywords: Rumor detection; Multi-modal; Graph attention network (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-734-2_99

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DOI: 10.2991/978-94-6463-734-2_99

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