Development of an interpretable classification method for healthcare disinformation detection: large language model feature extraction and ensemble learning

Authors

DOI:

https://doi.org/10.15587/1729-4061.2026.357119

Keywords:

disinformation detection, medical fake news, sentiment analysis, emotion recognition

Abstract

The object of this study is disinformation in the healthcare domain, distributed through social media and online news sources. Disinformation has become one of the most serious threats to individuals and societies, and it is particularly dangerous when it comes to medical disinformation. The rise of false medical claims online has overwhelmed human researchers, and automated detection methods suffer from several issues, such as low accuracy, the inability to explain solutions derived from implicit deep learning methods or relying on strict text features. This work proposes a method based on large language models (LLMs) and a machine learning (ML) approach for the explainable detection of disinformation in the healthcare sector and identifies attributes inherent in false statements, such as emotionality and rhetorical coloring. Large Language Model Meta AI (LLaMA) serves as a layer for identifying key features, and the ML approach classifies the text with explanations. Shapley Additive Explanations (SHAP) were applied to interpret individual predictions and identify which features contribute most to classification decisions. This method demonstrated high results on two publicly available datasets, achieving an F1 score of approximately 96%. The high performance is explained by the fact that medical disinformation relies on emotional manipulation and persuasive rhetorical patterns, which differ from the neutral tone of reliable medical content. Unlike existing approaches that achieve similar accuracy through opaque methods, the proposed approach relies on interpretable features and provides per-prediction explanations. The proposed method can be applied in automated content-moderation systems and public-health monitoring tools

Author Biographies

Vusal Shahbazov, Institute of Information Technology

PhD Student, Senior Software Developer

Department of Information Protection Methods and Systems

Vagif Mammadaliyev, Institute of Information Technology

PhD Student, Senior Software Developer

Department of Computer Science

References

  1. Wang, Y., McKee, M., Torbica, A., Stuckler, D. (2019). Systematic Literature Review on the Spread of Health-related Misinformation on Social Media. Social Science & Medicine, 240, 112552. https://doi.org/10.1016/j.socscimed.2019.112552
  2. van der Linden, S. (2022). Misinformation: susceptibility, spread, and interventions to immunize the public. Nature Medicine, 28 (3), 460–467. https://doi.org/10.1038/s41591-022-01713-6
  3. Borges do Nascimento, I. J., Beatriz Pizarro, A., Almeida, J., Azzopardi-Muscat, N., André Gonçalves, M. et al. (2022). Infodemics and health misinformation: a systematic review of reviews. Bulletin of the World Health Organization, 100 (9), 544–561. https://doi.org/10.2471/blt.21.287654
  4. Fridman, I., Johnson, S., Elston Lafata, J. (2023). Health Information and Misinformation: A Framework to Guide Research and Practice. JMIR Medical Education, 9, e38687. https://doi.org/10.2196/38687
  5. Suarez-Lledo, V., Alvarez-Galvez, J. (2021). Prevalence of Health Misinformation on Social Media: Systematic Review. Journal of Medical Internet Research, 23 (1), e17187. https://doi.org/10.2196/17187
  6. Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L. et al. (2020). The COVID-19 social media infodemic. Scientific Reports, 10 (1). https://doi.org/10.1038/s41598-020-73510-5
  7. Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H. (2017). Fake News Detection on Social Media. ACM SIGKDD Explorations Newsletter, 19 (1), 22–36. https://doi.org/10.1145/3137597.3137600
  8. Choraś, M., Demestichas, K., Giełczyk, A., Herrero, Á., Ksieniewicz, P., Remoundou, K. et al. (2021). Advanced Machine Learning techniques for fake news (online disinformation) detection: A systematic mapping study. Applied Soft Computing, 101, 107050. https://doi.org/10.1016/j.asoc.2020.107050
  9. Liu, Z., Zhang, T., Yang, K., Thompson, P., Yu, Z., Ananiadou, S. (2024). Emotion detection for misinformation: A review. Information Fusion, 107, 102300. https://doi.org/10.1016/j.inffus.2024.102300
  10. Pröllochs, N., Bär, D., Feuerriegel, S. (2021). Emotions explain differences in the diffusion of true vs. false social media rumors. Scientific Reports, 11 (1). https://doi.org/10.1038/s41598-021-01813-2
  11. Hamed, S. Kh., Ab Aziz, M. J., Yaakub, M. R. (2023). Fake News Detection Model on Social Media by Leveraging Sentiment Analysis of News Content and Emotion Analysis of Users’ Comments. Sensors, 23 (4), 1748. https://doi.org/10.3390/s23041748
  12. Murugesan, S., Pachamuthu, K. (2022). Fake News Detection in the Medical Field Using Machine Learning Techniques. International Journal of Safety and Security Engineering, 12 (6), 723–727. https://doi.org/10.18280/ijsse.120608
  13. Kolluri, N., Liu, Y., Murthy, D. (2022). COVID-19 Misinformation Detection: Machine-Learned Solutions to the Infodemic. JMIR Infodemiology, 2 (2), e38756. https://doi.org/10.2196/38756
  14. Iwendi, C., Mohan, S., khan, S., Ibeke, E., Ahmadian, A., Ciano, T. (2022). Covid-19 fake news sentiment analysis. Computers and Electrical Engineering, 101, 107967. https://doi.org/10.1016/j.compeleceng.2022.107967
  15. Sikosana, M., Maudsley-Barton, S., Ajao, O. (2025). Advanced Health Misinformation Detection Through Hybrid CNN-LSTM Models Informed by the Elaboration Likelihood Model (ELM). 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA), 1–11. https://doi.org/10.1109/acdsa65407.2025.11166406
  16. Alghamdi, J., Lin, Y., Luo, S. (2023). Towards COVID-19 fake news detection using transformer-based models. Knowledge-Based Systems, 274, 110642. https://doi.org/10.1016/j.knosys.2023.110642
  17. Guo, Y., Ovadje, A., Al-Garadi, M. A., Sarker, A. (2024). Evaluating large language models for health-related text classification tasks with public social media data. Journal of the American Medical Informatics Association, 31 (10), 2181–2189. https://doi.org/10.1093/jamia/ocae210
  18. Dima, A., Ilis, E., Florea, D., Dascalu, M. (2025). Detection of Fake News in Romanian: LLM-Based Approaches to COVID-19 Misinformation. Information, 16 (9), 796. https://doi.org/10.3390/info16090796
  19. Bojić, L., Zagovora, O., Zelenkauskaite, A., Vuković, V., Čabarkapa, M., Veseljević Jerković, S., Jovančević, A. (2025). Comparing large Language models and human annotators in latent content analysis of sentiment, political leaning, emotional intensity and sarcasm. Scientific Reports, 15 (1). https://doi.org/10.1038/s41598-025-96508-3
  20. Patwa, P., Sharma, S., Pykl, S., Guptha, V., Kumari, G., Akhtar, M. S. et al. (2021). Fighting an Infodemic: COVID-19 Fake News Dataset. Combating Online Hostile Posts in Regional Languages during Emergency Situation, 21–29. https://doi.org/10.1007/978-3-030-73696-5_3
  21. Cui, L., Seo, H., Tabar, M., Ma, F., Wang, S., Lee, D. (2020). DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 492–502. https://doi.org/10.1145/3394486.3403092
  22. Felber, T. (2021). Constraint 2021: Machine Learning Models for COVID-19 Fake News Detection Shared Task. arXiv. https://arxiv.org/abs/2101.03717
  23. Wani, A., Joshi, I., Khandve, S., Wagh, V., Joshi, R. (2021). Evaluating Deep Learning Approaches for Covid19 Fake News Detection. Combating Online Hostile Posts in Regional Languages during Emergency Situation, 153–163. https://doi.org/10.1007/978-3-030-73696-5_15
  24. Karnyoto, A. S., Sun, C., Liu, B., Wang, X. (2022). Augmentation and heterogeneous graph neural network for AAAI2021-COVID-19 fake news detection. International Journal of Machine Learning and Cybernetics, 13 (7), 2033–2043. https://doi.org/10.1007/s13042-021-01503-5
Development of an interpretable classification method for healthcare disinformation detection: large language model feature extraction and ensemble learning

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Published

2026-06-30

How to Cite

Shahbazov, V., & Mammadaliyev, V. (2026). Development of an interpretable classification method for healthcare disinformation detection: large language model feature extraction and ensemble learning. Eastern-European Journal of Enterprise Technologies, 3(2 (141), 87–100. https://doi.org/10.15587/1729-4061.2026.357119

Issue

Vol. 3 No. 2 (141) (2026): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2026 Vusal Shahbazov, Vagif Mammadaliyev

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