Open Access

ARTICLE

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

Soufiane Khedairia¹, Akram Bennour^2,*, Mouaaz Nahas³, Aida Chefrour¹, Rashiq Rafiq Marie⁴, Mohammed Al-Sarem⁵

1 LiM Laboratory, Department of Computer Science, Faculty of Science and Technology, University of Souk Ahras, Souk Ahras, 41000, Algeria
2 Laboratory of Mathematics, Informatics and Systems (LAMIS), Echahid Cheikh Larbi Tebessi University, Tebessa, 12000, Algeria
3 Departement of Electrical Engineering, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
4 Information Systems Department, College of Computer Science and Engineering, Taibah University, Medina, 41477, Saudi Arabia
5 Department of information Technology, Aylol University College, Yarim, 547, Yemen

* Corresponding Author: Akram Bennour. Email:

Computers, Materials & Continua 2025, 85(1), 1267-1285. https://doi.org/10.32604/cmc.2025.066601

Received 12 April 2025; Accepted 28 June 2025; Issue published 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 three modules. The first one creates a Graph Neural Network (GNN) based model to learn user profile properties, while the second module encodes news content, user historical posts, and news sharing cascading on social media as user preferences GNN-based model. The inter-dependencies between user profiles and user preferences are handled through the third module using a co-attention mechanism for capturing the relationship between the two GNN-based models. We conducted several experiments on two commonly used fake news datasets, Politifact and Gossipcop, where our approach achieved 98.53% accuracy on the Gossipcop dataset and 96.77% accuracy on the Politifact dataset. These results illustrate the effectiveness of the CMCG approach for fake news detection, as it combines various information from different modalities to achieve relatively high performances.

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Keywords

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