{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T20:00:30Z","timestamp":1785355230586,"version":"3.55.0"},"reference-count":54,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"\n Background<\/jats:title>\n Identifying traits of narcissistic personality disorder (NPD) is clinically challenging, yet early detection can significantly improve outcomes. Online forums have become a major source of self-expression, offering new opportunities to understand mental health. However, analyzing this complex language requires new tools.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aims to determine whether a machine learning model could be trained to reliably detect language patterns associated with NPD traits in Reddit posts. Specifically, we sought to identify both authors who exhibit these traits and posts discussing individuals with these traits.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We analyzed 75,985 posts from 4 Reddit communities (r\/Narcissism, r\/DeepThoughts, r\/Showerthoughts, and r\/ImposterSyndrome). A subset of 966 posts was annotated by 2 psychiatrists to create a reliable dataset. Using a range of machine learning techniques, from traditional text analysis to modern transformer-based embedding models, we trained the system to distinguish posts containing NPD-trait markers from general reflective posts. This secondary analysis of deidentified public Reddit data was exempted by the Bo\u011fazi\u00e7i University FMINAREK (Fen Bilimleri ve M\u00fchendislik Alanlar\u0131 \u0130nsan Ara\u015ft\u0131rmalar\u0131 Etik Kurulu) Institutional Review Board (exemption\/application number 2025-13).<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Our models demonstrated high accuracy. The modern embedding-based models were particularly effective, achieving a mean F1-score of 0.90, indicating a strong balance between recall and precision by correctly identifying posts with NPD-trait markers while minimizing false positives. The models remained effective even when common keywords such as \u201cnarcissism\u201d were removed, with a mean relative F1-score decrease of 3.6% for the frequency-based baseline classifier and less than 2% (1.9% and 0.8%) for the embedding models.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n This study demonstrates that automated analysis of online posts is a promising approach for understanding and identifying NPD traits. Although this technology has potential for future clinical applications, it is currently a research tool and should be used only with strict ethical oversight, not for public-facing diagnosis.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/75799","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T19:10:06Z","timestamp":1785352206000},"page":"e75799","source":"Crossref","is-referenced-by-count":0,"title":["Detecting Narcissistic Personality Disorder Traits on Forums: Proof-of-Concept Study"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0360-2982","authenticated-orcid":false,"given":"Ahmet 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