Development of an interpretable classification method for healthcare disinformation detection: large language model feature extraction and ensemble learning - Shahbazov, Vusal; Mammadaliyev, Vagif (Eastern-European Journal of Enterprise Technologies, 2026)
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