Cognitive Computing Frameworks for Scalable Deception Detection in Textual Data

Detecting deception in emotionally grounded natural language remains a significant challenge
due to the subtlety and context dependence of deceptive intent. In this work, we
use a structured behavioral dataset in which participants produce truthful and deceptive
statements under emotional and social constraints. To maintain label accuracy and semantic
consistency, we propose a multilayer validation pipeline combining self-consistency
prompting with feedback-guided revision, implemented through the CoTAM (Chain-of-
Thought Assisted Modification) method. Our results demonstrate that this framework
enhances deception detection by leveraging a sentence decomposition strategy that highlights
subtle emotional and strategic cues, improving interpretability for both models and
human annotators.