IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-734-2_99.html

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

In: Proceedings of the 2025 10th International Conference on Social Sciences and Economic Development (ICSSED 2025)

Author

Listed:
  • Shiming Li

    (Nanjing University of Aeronautics and Astronautics)

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.

Suggested Citation

  • Shiming Li, 2025. "A Multi-modal Rumor Detection Model Based on Temporal Graph Attention Network," Advances in Economics, Business and Management Research, in: Huaping Sun & Hang Luo & Vilas Gaikar & Natālija Cudečka-Puriņa (ed.), Proceedings of the 2025 10th International Conference on Social Sciences and Economic Development (ICSSED 2025), pages 890-905, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-734-2_99
    DOI: 10.2991/978-94-6463-734-2_99
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:advbcp:978-94-6463-734-2_99. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.