@inproceedings{treistman-etal-2026-profiling,
title = "Profiling Psychopathic Behavior Using Machine Learning",
author = "Treistman, Avi and
David, Tehilla and
Levi, Sivan and
Mughaz, Dror",
editor = {Kokkinakis, Dimitrios and
Themistocleous, Charalambos and
Dias, Ga{\"e}l and
Fraser, Kathleen C. and
{\"O}hman, Fredrik and
Pais, Sebasti{\~a}o},
booktitle = "Proceedings of the Sixth Resources and {P}rocess{I}ng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments in cooperation with the {MENTAL}.ai consortium",
month = may,
year = "2026",
address = "Palma, Mallorca, Spain",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.rapid-1.10/",
doi = "10.63317/2rto98ehmp4d",
pages = "115--125",
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."
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%0 Conference Proceedings
%T Profiling Psychopathic Behavior Using Machine Learning
%A Treistman, Avi
%A David, Tehilla
%A Levi, Sivan
%A Mughaz, Dror
%Y Kokkinakis, Dimitrios
%Y Themistocleous, Charalambos
%Y Dias, Gaël
%Y Fraser, Kathleen C.
%Y Öhman, Fredrik
%Y Pais, Sebastião
%S Proceedings of the Sixth Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments in cooperation with the MENTAL.ai consortium
%D 2026
%8 May
%I European Language Resources Association (ELRA)
%C Palma, Mallorca, Spain
%F treistman-etal-2026-profiling
%X 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.
%R 10.63317/2rto98ehmp4d
%U https://aclanthology.org/2026.rapid-1.10/
%U https://doi.org/10.63317/2rto98ehmp4d
%P 115-125
Markdown (Informal)
[Profiling Psychopathic Behavior Using Machine Learning](https://aclanthology.org/2026.rapid-1.10/) (Treistman et al., RaPID 2026)
ACL
- Avi Treistman, Tehilla David, Sivan Levi, and Dror Mughaz. 2026. Profiling Psychopathic Behavior Using Machine Learning. In Proceedings of the Sixth Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments in cooperation with the MENTAL.ai consortium, pages 115–125, Palma, Mallorca, Spain. European Language Resources Association (ELRA).