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  • Open Access

    ARTICLE

    A Co-Attention Mechanism into a Combined GNN-Based Model for Fake News Detection

    Soufiane Khedairia1, Akram Bennour2,*, Mouaaz Nahas3, Aida Chefrour1, Rashiq Rafiq Marie4, Mohammed Al-Sarem5

    CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1267-1285, 2025, DOI:10.32604/cmc.2025.066601 - 29 August 2025

    Abstract These days, social media has grown to be an integral part of people’s lives. However, it involves the possibility of exposure to “fake news,” which may contain information that is intentionally or inaccurately false to promote particular political or economic interests. The main objective of this work is to use the co-attention mechanism in a Combined Graph neural network model (CMCG) to capture the relationship between user profile features and user preferences in order to detect fake news and examine the influence of various social media features on fake news detection. The proposed approach includes… More >

  • Open Access

    ARTICLE

    Fake News Detection Based on Cross-Modal Message Aggregation and Gated Fusion Network

    Fangfang Shan1,2,*, Mengyao Liu1,2, Menghan Zhang1,2, Zhenyu Wang1,2

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1521-1542, 2024, DOI:10.32604/cmc.2024.053937 - 18 July 2024

    Abstract Social media has become increasingly significant in modern society, but it has also turned into a breeding ground for the propagation of misleading information, potentially causing a detrimental impact on public opinion and daily life. Compared to pure text content, multmodal content significantly increases the visibility and share ability of posts. This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection. To effectively address the critical challenge of accurately detecting fake news on social media, this paper proposes a fake… More >

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