Decoding Disinformation: A Feature-Driven Explainable AI Approach to Multi-Domain Fake News Detection

Digital misinformation presents a dual challenge: achieving high detection accuracy while ensuring interpretability. This paper introduces X-FRAME (Explainable FRAMing Engine), a hybrid framework that combines semantic representations from XLM-RoBERTa with theory-informed features related to psycholinguistic framing, source credibility, and social context. Unlike fact-checking systems that verify claims directly, X-FRAME detects linguistic, contextual, and stylistic indicators statistically associated with misinformation. Evaluated across eight publicly available datasets totaling 286,260 samples, X-FRAME achieves 86% accuracy and 81% recall on the minority Fake class, significantly outperforming text-only and features-only baselines. The model demonstrates cross-domain adaptability potential, attaining 97% accuracy on formal news articles and 72% on social media content. Importantly, X-FRAME provides transparent, human-understandable rationales via Local Interpretable Model-agnostic Explanations (LIME) and Permutation Importance, anchoring predictions in interpretable features such as sensationalism and speaker credibility. This work advances misinformation detection by unifying high performance with explainability and cross-domain adaptability.