A Multimodal Ensemble-Based Framework for Detecting Fake News Using Visual and Textual Features
Abdullah M. Zan H. Javed A. Sohail M. Mamyrbayev O. Turysbek Z. Eshkiki H. Caraffini F.
January 2026Multidisciplinary Digital Publishing Institute (MDPI)
Mathematics
2026#14Issue 2
Detecting fake news is essential in natural language processing to verify news authenticity and prevent misinformation-driven social, political, and economic disruptions targeting specific groups. A major challenge in multimodal fake news detection is effectively integrating textual and visual modalities, as semantic gaps and contextual variations between images and text complicate alignment, interpretation, and the detection of subtle or blatant inconsistencies. To enhance accuracy in fake news detection, this article introduces an ensemble-based framework that integrates textual and visual data using ViLBERT’s two-stream architecture, incorporates VADER sentiment analysis to detect emotional language, and uses Image–Text Contextual Similarity to identify mismatches between visual and textual elements. These features are processed through the Bi-GRU classifier, Transformer-XL, DistilBERT, and XLNet, combined via a stacked ensemble method with soft voting, culminating in a T5 metaclassifier that predicts the outcome for robustness. Results on the Fakeddit and Weibo benchmarking datasets show that our method outperforms state-of-the-art models, achieving up to 96% and 94% accuracy in fake news detection, respectively. This study highlights the necessity for advanced multimodal fake news detection systems to address the increasing complexity of misinformation and offers a promising solution.
deep learning , fake news detection , NLP , sentiment analysis , transformers
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School of Computer Science and Artificial Intelligence, Zhengzhou University, Zhengzhou, 450001, China
School of Information Science and Engineering, Hunan University, Changsha, 410082, China
Institute of Information and Computational Technologies, Almaty, 050010, Kazakhstan
Department of Computer Science, Swansea University, Swansea, SA1 8EN, United Kingdom
School of Computer Science and Artificial Intelligence
School of Information Science and Engineering
Institute of Information and Computational Technologies
Department of Computer Science
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