Uncovering social-contextual and individual mental health factors associated with violence via computational inference

Hernando Santamaría-García, Sandra Baez, Diego Mauricio Aponte-Canencio, Guido Orlando Pasciarello, Patricio Andrés Donnelly-Kehoe, Gabriel Maggiotti, Diana Matallana, Eugenia Hesse, Alejandra Neely, José Gabriel Zapata, Winston Chiong, Jonathan Levy, Jean Decety, Agustín Ibáñez*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)
47 Downloads (Pure)


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.
Original languageEnglish
Article number100176
Number of pages20
Issue number2
Publication statusPublished - 12 Feb 2021
MoE publication typeA1 Journal article-refereed


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