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Volume 13 | Issue 5 | Year 2026 | Article Id. IJECE-V13I5P122 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I5P122

Advancing Fake News Detection: Multimodal LSTM Optimized by Grey Wolf Algorithm with Explainable AI

Raed Abdul Karim Al-Jabri, Mohsen Rezvani

Received Revised Accepted Published
18 Feb 2026 18 Mar 2026 21 Apr 2026 27 May 2026

Citation :

Raed Abdul Karim Al-Jabri, Mohsen Rezvani, "Advancing Fake News Detection: Multimodal LSTM Optimized by Grey Wolf Algorithm with Explainable AI," International Journal of Electronics and Communication Engineering, vol. 13, no. 5, pp. 272-285, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I5P122

Abstract

Due to the social media explosion, the spread of misinformation is increasing, and it presents a severe social and political threat. These applications are typical of many fake news detection systems that rely solely on textual data and employ black-box models, which limit robustness and interpretability. In this paper, we introduce a new multimodal model that combines textual semantics, user behavior, and Propagation properties together in a unified framework. The semantic representation is learned using FastText embeddings, and the patterns of diffusion are represented by propagation-based features. An LSTM network based on the GWO algorithm achieves good classification for stable data. Shapley Additive Explanations (SHAP) is used to explain the model predictions and improve interpretability. We conduct empirical evaluation on Multi-Fake-DetectiVE 2023 and TAGFN, where our model matches the state-of-the-art performance with up to 99.32% accuracy in non-adversarial settings and outperforms other techniques by several orders of magnitude under adversarial setups. Concluding Remarks: We have demonstrated that the combination of multimodal learning, evolutionary optimization, and explainable AI guarantees strong amounts of robustness in detecting fake news.

Keywords

Fake News Detection, Multimodal Learning, Fasttext Embeddings, Long Short-Term Memory (Lstm), Grey Wolf Optimizer (GWO), SHAP.

References

  1. Faisal A. Alshuwaier, and Fawaz A. Alsulaiman, “Fake News Detection Using Machine Learning and Deep Learning Algorithms: A Comprehensive Review and Future Perspectives,” Computers, vol. 14, no. 9, pp. 1-40, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  2. Pingping Yang et al., “Multi-Modal Transformer for Fake News Detection,” Mathematical Biosciences and Engineering, vol. 20, no. 8, pp. 14699-14717, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  3. Jawaher Alghamdi, Yuqing Lin, and Suhuai Luo, “A Comparative Study of Machine Learning and Deep Learning Techniques for Fake News Detection,” Information, vol. 13, no. 12, pp. 1-28, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  4. Carmela Comito, Luciano Caroprese, and Ester Zumpano, “Multimodal Fake News Detection on Social Media: A Survey of Deep Learning Techniques,” Social Network Analysis and Mining, vol. 13, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  5. Alim Al Ayub Ahmed et al., “Detecting Fake News Using Machine Learning: A Systematic Literature Review,” arXiv preprint, pp. 1-10, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  6. G. Bharath et al., “Detecting Fake News Using Machine Learning Algorithms,” 2021 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, pp. 1-5, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  7. Hounaida Moalla et al., “Exploring the Power of Dual Deep Learning for Fake News and AI-Generated News Detection,” Informatica, vol. 48, no. 4, pp. 567-594, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  8. Amila Silva et al., “Unsupervised Domain-agnostic Fake News Detection Using Multi-modal Weak Signals,” arXiv preprint, pp. 1-15, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  9. Jiaying Wu, Jiafeng Guo, and Bryan Hooi, “Fake News in Sheep’s Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks,” Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona Spain, pp. 3367-3378, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  10. M. F. Mridha et al., “A Comprehensive Review on Fake News Detection with Deep Learning,” IEEE Access, vol. 9, pp. 156151-156170, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  11. Changhe Song et al., “CED: Credible Early Detection of Social Media Rumors,” IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 8, pp. 3035-3047, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  12. Arun Kumar Yadav et al., “Fake News Detection using Hybrid Deep Learning Method,” SN Computer Science, vol. 4, pp. 1-20, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  13. Yexin Tian et al., “An Empirical Comparison of Machine Learning and Deep Learning Models for Automated Fake News Detection,” Mathematics, vol. 13, no. 13, pp. 1-24, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  14. Khurram Shahzad et al., “A Scoping Review of the Relationship of Big Data Analytics with Context-Based Fake News Detection on Digital Media in Data Age,” Sustainability, vol. 14, no. 21, pp. 1-25, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  15. Vaishali U. Gongane, Mousami V. Munot, and Alwin D. Anuse, “A Survey of Explainable AI Techniques for Detection of Fake News and Hate Speech on Social Media Platforms,” Journal of Computational Social Science, vol. 7, pp. 587-623, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  16. Ajay Kumar, and James W. Taylor, “Feature Importance in the Age of Explainable AI: Case Study of Detecting Fake News & Misinformation via a Multi-modal Framework,” European Journal of Operational Research, vol. 317, no. 2, pp. 401-413, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  17. Ziyi Zhou et al., “FineFake: A knowledge-enriched Dataset for Fine-grained Multi-domain Fake News Detection,” Information Fusion, vol. 132, pp. 1-12, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  18. Lidong Wang et al., “Multimodal Fusion with LLM Content via Hierarchical Progressive Transformer for Explainable Fake News Detection,” Information Processing & Management, vol. 63, no. 5, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  19. Pervaiz Akhtar et al., “Detecting Fake News and Disinformation using Artificial Intelligence and Machine Learning to Avoid Supply Chain Disruptions,” Annals of Operations Research, vol. 327, pp. 633-657, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  20. A. Kumari Shalini, Sameer Saxena, and B. Suresh Kumar, “Designing A Model for Fake News Detection in Social Media Using Machine Learning Techniques,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 2s, pp. 218-226, 2023.
    [Google Scholar] [Publisher Link]
  21. Ciprian-Octavian Truic, and Elena-Simona Apostol, “It’s all in the Embedding! Fake News Detection Using Document Embeddings,” Mathematics, vol. 11, no. 3, pp. 1-29, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  22. Sahar Tahmasebi, Eric Müller-Budack, and Ralph Ewerth, “Robust Fake News Detection using Large Language Models under Adversarial Sentiment Attacks,” Proceedings of the ACM Web Conference, Dubai United Arab Emirates, pp. 1717-1726, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  23. Pawan Kumar Verma et al., “MCred: Multi-modal Message Credibility for Fake News Detection using BERT and CNN,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, pp. 10617-10629, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  24. Zhiwei Guo et al., “A Novel Fake News Detection Model for Context of Mixed Languages Through Multiscale Transformer,” IEEE Transactions on Computational Social Systems, vol. 11, no. 4, pp. 5079-5089, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  25. Jing He et al., “FACTGUARD: Event-Centric and Commonsense-Guided Fake News Detection,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 40, no. 1, pp. 363-371, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  26. Shankar Biradar, Sunil Saumya, and Arun Chauhan, “Combating the Infodemic: COVID-19 Induced Fake News Recognition in Social Media Networks,” Complex & Intelligent Systems, vol. 9, pp. 2879-2891, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  27. Junfei Wu et al., “Adversarial Contrastive Learning for Evidence-Aware Fake News Detection with Graph Neural Networks,” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 11, pp. 5591-5604, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  28. Lixia Ji et al., “A Local Dynamic Propagation Graph-based Method for Multi-modal Fake News Detection,” Journal of Intelligent Information Systems, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  29. Alessandro Bondielli et al., “MULTI-Fake-DetectiVE at EVALITA 2023: Overview of the MULTImodal Fake News Detection and VErification Task,” Proceedings 8th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian (EVALITA 2023), Parma, Italy, vol. 3473, pp. 1-8, 2023.
    [    Google Scholar] [Publisher Link]
  30. Kay Liu et al., “TAGFN: A Text-Attributed Graph Dataset for Fake News Detection in the Age of LLMs,” arXiv preprint, pp. 1-16, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  31. Siyu Lu et al., “Multiscale Feature Extraction and Fusion of Image and Text in VQA,” International Journal of Computational Intelligence Systems, vol. 16, pp. 1-11, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  32. Delvin Ce Zhang, et al., “Text-Attributed Graph Representation Learning: Methods, Applications, and Challenges,” Companion Proceedings of the ACM Web Conference 2024, Singapore, pp. 1298-1301, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  33. Saif Alsudani et al., “Enhancing Spam Detection: A Crow-Optimized FFNN with LSTM for Email Security,” Wasit Journal of Computer and Mathematics Science, vol. 3, no. 1, pp. 28-39, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  34. Prokash Gogoi, and J. Arul Valan, “Enhancing Date Fruit Classification using Machine Learning, CTGAN, and SHAP-based Explainability,” Journal of Food Measurement and Characterization, vol. 19, pp. 6851-6872, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  35. Saif Wali Ali Alsudani, and Ghassan Khudair Saud, “Recurrent Neural Network Optimized by Grasshopper for Accurate Audio Data-Based Diagnosis of Parkinson's Disease,” Wasit Journal for Pure Sciences, vol. 4, no. 2, pp. 56-75, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  36. Hao Chen et al., “A Self-learning Multimodal Approach for Fake News Detection,” Frontiers in Artificial Intelligence, vol. 8, pp. 1-25, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  37. Rajalakshmi Viswanathan et al., “Detection of Fake News in Social Media using CNN with Grey Wolf Optimized BERT,” Acta Scientiarum. Technology, vol. 47, pp. 1-11, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  38. Haji Gul et al., “Advancing Fake News Detection with Graph Neural Network and Deep Learning,” Journal of Physics: Complexity, vol. 6, no. 2, pp. 1-14, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  39. Fuqiang You et al., “ProFNSE: Propagation Dynamics-derived Fake News Detection in Social Networks,” The Journal of Supercomputing, vol. 81, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  40. Sanya Bansal, Harshit Panwar, and Geetika Munjal, “Detecting Fake News with XAI: A Look at SHAP for Making Machine Learning Models More Explainable,” 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON), Pune, India, pp. 1-6, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]