Year: 2026
Type: conference-paper
Abstract: Psychopathy is a complex personality disorder characterized by persistent deficits in empathy and manipulative behavior.Traditional diagnostic methods often rely on subjective clinical assessments, which are susceptible to deception.This research proposes an objective, non-invasive computational framework for profiling psychopathic traits using Natural Language Processing (NLP) and Machine Learning.We developed a systematic pipeline utilizing transcribed interviews from confirmed criminal psychopaths and a balanced control group.To address data sparsity and noise, we employed the Dynamic Variance Thresholding (DyVaT) algorithm to construct a semantically dense vocabulary of over 1,300 features.The methodology integrates advanced preprocessing, TF-IDF vectorization, and synonym-based data augmentation to enhance model generalization.Among the evaluated classifiers, a Linear Support Vector Machine (SVM) achieved the highest performance, with an accuracy of 0.8081 and an F1-score of 0.7957.Our findings demonstrate the efficacy of linguistic biomarkers and feature importance analysis in distinguishing psychopathic speech patterns.This study provides a scalable methodology for early screening and diagnostics, with significant implications for forensic psychology, security, and ethical AI deployment in mental health.
Institution: Jerusalem College of Technology
Language: English
Subfield: Clinical Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7166530768