{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:42:50Z","timestamp":1760575370457,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>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 selfconsistency 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.<\/jats:p>","DOI":"10.3390\/bdcc9100260","type":"journal-article","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T14:34:15Z","timestamp":1760452455000},"page":"260","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cognitive Computing Frameworks for Scalable Deception Detection in Textual Data"],"prefix":"10.3390","volume":"9","author":[{"given":"Faiza","family":"Belbachir","sequence":"first","affiliation":[{"name":"LYRIDS ECE-Paris, 10 Rue Sextius Michel, 75015 Paris, France"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"110201","DOI":"10.1016\/j.isci.2024.110201","article-title":"Lie detection algorithms disrupt the social dynamics of accusation behavior","volume":"27","author":"Klockmann","year":"2024","journal-title":"iScience"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1177\/0146167203029005010","article-title":"Lying words: Predicting deception from linguistic styles","volume":"29","author":"Newman","year":"2003","journal-title":"Personal. 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