Abstract

This research delves into the intricate landscape of deception detection, employing computational linguistic techniques to identify and analyze linguistic cues indicative of deceptive communication. Focusing on multimodal contexts, this study investigates the interplay between language, sentiment, and pragmatic elements in revealing deceptive intent. We propose a novel framework that integrates sentiment analysis, discourse analysis, and machine learning algorithms to detect deception with improved accuracy. The methodology involves analyzing a large corpus of text and audio-visual data, extracting relevant linguistic features, and training machine learning models to classify deceptive and truthful statements. The results demonstrate the effectiveness of our approach in identifying subtle linguistic patterns associated with deception, contributing significantly to the advancement of automated deception detection systems. The study concludes by highlighting the implications of these findings for various fields, including law enforcement, cybersecurity, and human-computer interaction, and outlines future research directions aimed at enhancing the robustness and generalizability of deception detection models.