Citation

Santamaría-García H, Baez S, Aponte-Canencio DM, Pasciarello GO, Donnelly-Kehoe PA, Maggiotti G, Matallana D, Hesse E, Neely A, Zapata JG, Chiong W, Levy J, Decety J, Ibáñez A. Patterns (N Y) 2021; 2(2): e100176.

Copyright

(Copyright © 2021, Cell Press)

DOI

10.1016/j.patter.2020.100176

PMID

33659906

Abstract

The identification of human violence determinants has sparked multiple questions from different academic fields. Innovative methodological assessments of the weight and interaction of multiple determinants are still required. Here, we examine multiple features potentially associated with confessed acts of violence in ex-members of illegal armed groups in Colombia (N = 26,349) through deep learning and feature-derived machine learning. We assessed 162 social-contextual and individual mental health potential predictors of historical data regarding consequentialist, appetitive, retaliative, and reactive domains of violence. Deep learning yields high accuracy using the full set of determinants. Progressive feature elimination revealed that contextual factors were more important than individual factors. Combined social network adversities, membership identification, and normalization of violence were among the more accurate social-contextual factors. To a lesser extent the best individual factors were personality traits (borderline, paranoid, and antisocial) and psychiatric symptoms. The results provide a population-based computational classification regarding historical assessments of violence in vulnerable populations.

Language: en

Keywords

violence; mental health; mental disorders; personality traits; deep neural networks; ex-members of illegal armed groups; machine learning methods; social adversity; social resources