Interpretation of Deep Learning Models in Natural Language Processing for Misinformation Detection with the Explainable AI (XAI) Approach
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Deep Learning, Explainable AI, Misinformation Detection, Model Interpretability, Natural Language Processing
Abstract
The increasing spread of misinformation through digital platforms has raised significant concerns about its societal impact, particularly in political, health, and social domains. Deep learning models in Natural Language Processing (NLP) have shown high performance in detecting misinformation, but their lack of interpretability remains a major challenge for trust, transparency, and accountability. As black-box models, they often fail to provide insights into how predictions are made, limiting their acceptance in sensitive real-world applications. This study investigates the integration of Explainable Artificial Intelligence (XAI) techniques to enhance the interpretability of deep learning models used in misinformation detection. The primary objective of this research is to evaluate how different XAI methods can be applied to explain and interpret the decisions of NLP-based misinformation classifiers. A comparative analysis was conducted using state-of-the-art deep learning models such as BERT and LSTM on benchmark datasets, including FakeNewsNet and LIAR. XAI methods including SHAP (SHapley Additive Explanations), LIME (Local Interpretable Model-agnostic Explanations), and attention visualization were applied to analyze model behavior and feature importance. The findings reveal that while deep learning models achieve high accuracy in misinformation detection, XAI methods significantly improve transparency by highlighting influential words and phrases contributing to model decisions. SHAP and LIME proved particularly effective in providing human-understandable explanations, aiding both developers and end-users. In conclusion, incorporating XAI into NLP-based misinformation detection frameworks enhances model interpretability without sacrificing performance, paving the way for more responsible and trustworthy AI deployment in combating online misinformation.
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References
Abdullah, M. (2024). Explainable deep learning model for stock price forecasting using textual analysis. Expert Systems with Applications, 249(Query date: 2025-05-03 19:56:09). https://doi.org/10.1016/j.eswa.2024.123740
Africano, B. (2024). PII Detection in Low-Resource Languages Using Explainable Deep Learning Techniques. ACM International Conference Proceeding Series, Query date: 2025-05-03 19:56:09, 94–103. https://doi.org/10.1145/3675888.3676036
Agerri, R. (2023). HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicine. CEUR Workshop Proceedings, 3516(Query date: 2025-05-03 19:56:09), 65–69.
Aleqabie, H. J. (2024). A Review Of Text Mining Techniques: Trends, and Applications In Various Domains. Iraqi Journal for Computer Science and Mathematics, 5(1), 125–141. https://doi.org/10.52866/ijcsm.2024.05.01.009
Amato, F. (2022). A Survey on Neural Recommender Systems: Insights from a Bibliographic Analysis. Lecture Notes in Networks and Systems, 451(Query date: 2025-05-03 19:56:09), 104–114. https://doi.org/10.1007/978-3-030-99619-2_10
Amin, K. (2020). DeepKAF: A Heterogeneous CBR Deep Learning Approach for NLP Prototyping. INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings, Query date: 2025-05-03 19:56:09. https://doi.org/10.1109/INISTA49547.2020.9194679
Ankalaki, S. (2025). Cyber Attack Prediction: From Traditional Machine Learning to Generative Artificial Intelligence. IEEE Access, 13(Query date: 2025-05-03 19:56:09), 44662–44706. https://doi.org/10.1109/ACCESS.2025.3547433
Ao, S. I. (2025). Cognitive Computing and Business Intelligence Applications in Accounting, Finance and Management. Big Data and Cognitive Computing, 9(3). https://doi.org/10.3390/bdcc9030054
Banafa, A. (2023). Transformative AI: Responsible, Transparent, and Trustworthy AI Systems. In Transformative AI: Responsible, Transparent, and Trustworthy AI Systems (p. 156). https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85180544759&origin=inward
Bhatt, A. (2021). DICE: A Drug Indication Classification and Encyclopedia for AI-Based Indication Extraction. Frontiers in Artificial Intelligence, 4(Query date: 2025-05-03 19:56:09). https://doi.org/10.3389/frai.2021.711467
Binbeshr, F. (2025). The Rise of Cognitive SOCs: A Systematic Literature Review on AI Approaches. IEEE Open Journal of the Computer Society, 6(Query date: 2025-05-03 19:56:09), 360–379. https://doi.org/10.1109/OJCS.2025.3536800
Costa, J. (2020). Fraunhofer AICOS at CLEF eHealth 2020 Task 1: Clinical Code Extraction from Textual Data Using Fine-Tuned BERT Models. CEUR Workshop Proceedings, 2696(Query date: 2025-05-03 19:56:09). https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85121828847&origin=inward
Díaz-Rodríguez, N. (2020). Accessible Cultural Heritage through Explainable Artificial Intelligence. UMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, Query date: 2025-05-03 19:56:09, 317–324. https://doi.org/10.1145/3386392.3399276
Dipto, S. M. (2023). An XAI Integrated Identification System of White Blood Cell Type Using Variants of Vision Transformer. Lecture Notes in Networks and Systems, 721(Query date: 2025-05-03 19:56:09), 303–315. https://doi.org/10.1007/978-3-031-35308-6_26
Dong, Z. (2023). Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling. Cancers, 15(17). https://doi.org/10.3390/cancers15174210
Dubey, A. (2024). AI Readiness in Healthcare through Storytelling XAI. CEUR Workshop Proceedings, 3831(Query date: 2025-05-03 19:56:09). https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85210866706&origin=inward
Durrani, U. K. (2024). A Decade of Progress: A Systematic Literature Review on the Integration of AI in Software Engineering Phases and Activities (2013-2023). IEEE Access, 12(Query date: 2025-05-03 19:56:09), 171185–171204. https://doi.org/10.1109/ACCESS.2024.3488904
Ebrahimi, A. (2024). Identification of patients’ smoking status using an explainable AI approach: A Danish electronic health records case study. BMC Medical Research Methodology, 24(1). https://doi.org/10.1186/s12874-024-02231-4
Erdo?any?lmaz, C. (2024). A New Explainable AI Approach to Legal Judgement Prediction: Detecting Model Uncertainty and Analyzing the Alignment between Judges and Models. 2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024, Query date: 2025-05-03 19:56:09. https://doi.org/10.1109/ASYU62119.2024.10757009
Erliksson, K. F. (2021). Cross-Domain Transfer of Generative Explanations Using Text-to-Text Models. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12801(Query date: 2025-05-03 19:56:09), 76–89. https://doi.org/10.1007/978-3-030-80599-9_8
Faruque, S. H. (2025). Decision support system to reveal future career over students’ survey using explainable AI. Education and Information Technologies, Query date: 2025-05-03 19:56:09. https://doi.org/10.1007/s10639-025-13361-7
Fenza, G. (2024). Robustness of models addressing Information Disorder: A comprehensive review and benchmarking study. Neurocomputing, 596(Query date: 2025-05-03 19:56:09). https://doi.org/10.1016/j.neucom.2024.127951
Fiok, K. (2020). Predicting the volume of response to tweets posted by a single twitter account. Symmetry, 12(6), 1–15. https://doi.org/10.3390/sym12061054
Gao, Y. (2024). Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning. ACM Computing Surveys, 56(7), 1–39. https://doi.org/10.1145/3644073
Gin, B. C. (2022). Exploring how feedback reflects entrustment decisions using artificial intelligence. Medical Education, 56(3), 303–311. https://doi.org/10.1111/medu.14696
Gurrapu, S. (2022). ExClaim: Explainable Neural Claim Verification Using Rationalization. Proceedings - 2022 IEEE 29th Annual Software Technology Conference, STC 2022, Query date: 2025-05-03 19:56:09, 19–26. https://doi.org/10.1109/STC55697.2022.00012
Hassan, M. (2024). Unfolding Explainable AI for Brain Tumor Segmentation. Neurocomputing, 599(Query date: 2025-05-03 19:56:09). https://doi.org/10.1016/j.neucom.2024.128058
Holzinger, A. (2019). Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data. European Journal of Nuclear Medicine and Molecular Imaging, 46(13), 2722–2730. https://doi.org/10.1007/s00259-019-04382-9
Jeshmol, P. J. (2025). A CLIP-based Video Question Answering framework with Explainable AI. 2025 IEEE International Students’ Conference on Electrical, Electronics and Computer Science, SCEECS 2025, Query date: 2025-05-03 19:56:09. https://doi.org/10.1109/SCEECS64059.2025.10940190
Karas, V. (2020). Deep learning for sentiment analysis: An overview and perspectives. Natural Language Processing for Global and Local Business, Query date: 2025-05-03 19:56:09, 97–132. https://doi.org/10.4018/978-1-7998-4240-8.ch005
Kavasidis, I. (2023). History of AI in Clinical Medicine. AI in Clinical Medicine: A Practical Guide for Healthcare Professionals, Query date: 2025-05-03 19:56:09, 41–48. https://doi.org/10.1002/9781119790686.ch4
Kim, B. (2020). Transparency and accountability in AI decision support: Explaining and visualizing convolutional neural networks for text information. Decision Support Systems, 134(Query date: 2025-05-03 19:56:09). https://doi.org/10.1016/j.dss.2020.113302
Kothadiya, D. R. (2023). SignExplainer: An Explainable AI-Enabled Framework for Sign Language Recognition With Ensemble Learning. IEEE Access, 11(Query date: 2025-05-03 19:56:09), 47410–47419. https://doi.org/10.1109/ACCESS.2023.3274851
Levich, S. (2023). Utilizing the omnipresent: Incorporating digital documents into predictive process monitoring using deep neural networks. Decision Support Systems, 175(Query date: 2025-05-03 19:56:09). https://doi.org/10.1016/j.dss.2023.114043
Liu, Y. (2024). Leveraging ChatGPT to optimize depression intervention through explainable deep learning. Frontiers in Psychiatry, 15(Query date: 2025-05-03 19:56:09). https://doi.org/10.3389/fpsyt.2024.1383648
Lorente, M. P. S. (2021). Explaining deep learning-based driver models. Applied Sciences (Switzerland), 11(8). https://doi.org/10.3390/app11083321
Madan, S. (2024). Transformer models in biomedicine. BMC Medical Informatics and Decision Making, 24(1). https://doi.org/10.1186/s12911-024-02600-5
Madsen, A. G. (2023). Concept-Based Explainability for an EEG Transformer Model. IEEE International Workshop on Machine Learning for Signal Processing, MLSP, 2023(Query date: 2025-05-03 19:56:09). https://doi.org/10.1109/MLSP55844.2023.10285992
Mazhar, K. (2024). A Survey on Methods for Explainability in Deep Learning Models. Learning and Analytics in Intelligent Systems, 40(Query date: 2025-05-03 19:56:09), 257–277. https://doi.org/10.1007/978-3-031-65392-6_23
Mersha, M. A. (2025). Evaluating the effectiveness of XAI techniques for encoder-based language models. Knowledge-Based Systems, 310(Query date: 2025-05-03 19:56:09). https://doi.org/10.1016/j.knosys.2025.113042
Nguyen, T. T. (2024). Effects of Common Sense and Supporting Texts for the Important Words in Solving Text Entailment Tasks—A Study on the e-SNLI Dataset. 2024 IEEE 15th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2024, Query date: 2025-05-03 19:56:09, 650–655. https://doi.org/10.1109/UEMCON62879.2024.10754725
Pospelova, N. (2024). Explainable Artificial Intelligence and Natural Language Processing for Unraveling Deceptive Contents. Fusion: Practice and Applications, 14(2), 146–158. https://doi.org/10.54216/FPA.140212
Saarela, K. (2023). Work Disability Risk Prediction Using Machine Learning. Studies in Computational Intelligence, 1112(Query date: 2025-05-03 19:56:09), 345–359. https://doi.org/10.1007/978-3-031-42112-9_16
Salmi, S. (2024). The Most Effective Interventions for Classification Model Development to Predict Chat Outcomes Based on the Conversation Content in Online Suicide Prevention Chats: Machine Learning Approach. JMIR Mental Health, 11(Query date: 2025-05-03 19:56:09). https://doi.org/10.2196/57362
Wahid, J. A. (2025). AI-driven social media text analysis during crisis: A review for natural disasters and pandemics. Applied Soft Computing, 171(Query date: 2025-05-03 19:56:09). https://doi.org/10.1016/j.asoc.2025.112774
Yu, J. (2022). Efficient Uncertainty Quantification for Multilabel Text Classification. Proceedings of the International Joint Conference on Neural Networks, 2022(Query date: 2025-05-03 19:56:09). https://doi.org/10.1109/IJCNN55064.2022.9892871
Zugarini, A. (2023). SAGE: Semantic-Aware Global Explanations for Named Entity Recognition. Proceedings of the International Joint Conference on Neural Networks, 2023(Query date: 2025-05-03 19:56:09). https://doi.org/10.1109/IJCNN54540.2023.10191364
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Copyright (c) 2025 mas'ud muhammadiah, Rashid Rahman, Sun Wei
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