Combining transformer, Bi-LSTM and CNN for efficient Vietnamese fake news detection

Fake news spreading widely on digital platforms presents challenges where the rapidly evolving information landscape lacks effective detection. This paper introduces a hybrid deep learning model that integrates Transformer, Bidirectional Long Short-Term Memory (Bi-LSTM), and Convolutional Neural Network architectures to jointly capture both semantic and structural characteristics of Vietnamese text for the task of fake news detection. A high-quality dataset comprising 2,336 manually annotated Vietnamese news articles was developed, spanning three major domains: Politics, Healthcare, and Society. The proposed model was trained and evaluated on this dataset and benchmarked against two widely adopted baseline models: Gated Recurrent Unit and Bi-LSTM. Experimental results indicate that the proposed model achieves superior performance, attaining an overall accuracy of 95.3% and an F1-score of 0.951. These findings underscore the efficacy of combining multi-layered linguistic representations in enhancing Vietnamese fake news detection and contribute a valuable annotated resource for future studies in this domain.