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Open Access

ARTICLE

Q-ALIGNer: A Quantum Entanglement-Driven Multimodal Framework for Robust Fake News Detection

Sara Tehsin1,*, Inzamam Mashood Nasir1, Wiem Abdelbaki2, Fadwa Alrowais3, Reham Abualhamayel4, Abdulsamad Ebrahim Yahya5, Radwa Marzouk6
1 Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania
2 College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait
3 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
4 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
5 Department of Information Technology, College of Computing and Information Technology, Northern Border University, Arar, Saudi Arabia
6 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
* Corresponding Author: Sara Tehsin. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.076514

Received 21 November 2025; Accepted 07 January 2026; Published online 13 February 2026

Abstract

The rapid proliferation of multimodal misinformation on social media demands detection frameworks that are not only accurate but also robust to noise, adversarial manipulation, and semantic inconsistency between modalities. Existing multimodal fake news detection approaches often rely on deterministic fusion strategies, which limits their ability to model uncertainty and complex cross-modal dependencies. To address these challenges, we propose Q-ALIGNer, a quantum-inspired multimodal framework that integrates classical feature extraction with quantum state encoding, learnable cross-modal entanglement, and robustness-aware training objectives. The proposed framework adopts quantum formalism as a representational abstraction, enabling probabilistic modeling of multimodal alignment while remaining fully executable on classical hardware. Q-ALIGNer is evaluated on four widely used benchmark datasets—FakeNewsNet, Fakeddit, Weibo, and MediaEval VMU—covering diverse platforms, languages, and content characteristics. Experimental results demonstrate consistent performance improvements over strong text-only, vision-only, multimodal, and quantum-inspired baselines, including BERT, RoBERTa, XLNet, ResNet, EfficientNet, ViT, Multimodal-BERT, ViLBERT, and QEMF. Q-ALIGNer achieves accuracies of 91.2%, 92.9%, 91.7%, and 92.1% on FakeNewsNet, Fakeddit, Weibo, and MediaEval VMU, respectively, with F1-score gains of 3–4 percentage points over QEMF. Robustness evaluation shows a reduced adversarial accuracy gap of 2.6%, compared to 7%–9% for baseline models, while calibration analysis indicates improved reliability with an expected calibration error of 0.031. In addition, computational analysis shows that Q-ALIGNer reduces training time to 19.6 h compared to 48.2 h for QEMF at a comparable parameter scale. These results indicate that quantum-inspired alignment and entanglement can enhance robustness, uncertainty awareness, and efficiency in multimodal fake news detection, positioning Q-ALIGNer as a principled and practical content-centric framework for misinformation analysis.

Keywords

Machine learning; fake news detection; multimodal learning; quantum natural language processing; cross-modal entanglement; adversarial robustness; uncertainty calibration

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