This paper presents Trust Sense, an explainable artificial intelligence system designed to detect psychological manipulation in digital content, including text, audio, and video. The proposed approach integrates natural language processing, multimodal analysis, and cognitive psychology principles to evaluate content credibility beyond traditional fake news detection methods. The system introduces a novel Manipulation Index (MI) and a Trust Score (TS), combining emotional intensity, urgency signals, bias indicators, and persuasive patterns. The architecture is based on a modular and scalable design, supporting real-time analysis and future integration into automated media pipelines such as AI-generated news broadcasting. Experimental results demonstrate strong performance across multiple evaluation metrics, highlighting the system’s ability to identify manipulative patterns and provide interpretable outputs. This work contributes to the field of explainable AI (XAI) by bridging technical analysis with psychological interpretation, offering a new perspective on misinformation detection. This paper is a preprint and has not yet undergone peer review.
Hassen Hamrouni (Thu,) studied this question.
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