TY - JOUR
T1 - Uncovering social-contextual and individual mental health factors associated with violence via computational inference
AU - Santamaría-García, Hernando
AU - Baez, Sandra
AU - Aponte-Canencio, Diego Mauricio
AU - Pasciarello, Guido Orlando
AU - Donnelly-Kehoe, Patricio Andrés
AU - Maggiotti, Gabriel
AU - Matallana, Diana
AU - Hesse, Eugenia
AU - Neely, Alejandra
AU - Zapata, José Gabriel
AU - Chiong, Winston
AU - Levy, Jonathan
AU - Decety, Jean
AU - Ibáñez, Agustín
N1 - Publisher Copyright:
© 2020 The Authors
PY - 2021/2/12
Y1 - 2021/2/12
N2 - 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. We assessed a comprehensive group of social-contextual and individual mental health factors to classify confessed acts of violence committed in the past among a large sample of Colombian ex-members of illegal armed groups (N = 26,349). We used a novel data-driven approach to classify subjects based on four confessed domains of violence (DoVs) and including two groups, (1) ex-members who admitted violent acts and (2) ex-members who denied violence in each DoV, matched by sex, age, and education stage. We found that accurate classification required both social-contextual and individual mental health factors, although the social-contextual factors were the most relevant. Our study provides population-based evidence on the factors associated with historical assessments of violence and describes a powerful analytical approach. This study opens up a new agenda for developing computational approaches for situated, multidimensional, and evidence-based assessments of violence. The study of human violence calls for methodological innovations. Here, we examined historical records for a large sample of ex-members of illegal armed groups in Colombia (N = 26,349) and combined deep learning and machine learning methods to identify the most relevant factors (>160) associated with different confessed domains of violence (DoVs). Results showed that accurate DoV classification required a combination of both social-contextual and individual mental health factors. The results support the development of computational approaches for multidimensional assessments of confessed DoV.
AB - 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. We assessed a comprehensive group of social-contextual and individual mental health factors to classify confessed acts of violence committed in the past among a large sample of Colombian ex-members of illegal armed groups (N = 26,349). We used a novel data-driven approach to classify subjects based on four confessed domains of violence (DoVs) and including two groups, (1) ex-members who admitted violent acts and (2) ex-members who denied violence in each DoV, matched by sex, age, and education stage. We found that accurate classification required both social-contextual and individual mental health factors, although the social-contextual factors were the most relevant. Our study provides population-based evidence on the factors associated with historical assessments of violence and describes a powerful analytical approach. This study opens up a new agenda for developing computational approaches for situated, multidimensional, and evidence-based assessments of violence. The study of human violence calls for methodological innovations. Here, we examined historical records for a large sample of ex-members of illegal armed groups in Colombia (N = 26,349) and combined deep learning and machine learning methods to identify the most relevant factors (>160) associated with different confessed domains of violence (DoVs). Results showed that accurate DoV classification required a combination of both social-contextual and individual mental health factors. The results support the development of computational approaches for multidimensional assessments of confessed DoV.
KW - DSML 5: Mainstream: Data science output is well understood and (nearly) universally adopted
KW - deep neural networks
KW - ex-members of illegal armed groups
KW - machine learning methods
KW - mental disorders
KW - mental health
KW - personality traits
KW - social adversity
KW - social resources
KW - violence
UR - http://www.scopus.com/inward/record.url?scp=85100771024&partnerID=8YFLogxK
U2 - 10.1016/j.patter.2020.100176
DO - 10.1016/j.patter.2020.100176
M3 - Article
AN - SCOPUS:85100771024
SN - 2666-3899
VL - 2
JO - Patterns
JF - Patterns
IS - 2
M1 - 100176
ER -