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Risk Matrix for Violent Radicalization: A Machine Learning Approach

Hypothesis-driven approaches identified important characteristics that differentiate violent from non-violent radicals. However, they produced a mosaic of explanations as they investigated a restricted number of preselected variables. Here we analyzed without a priory assumption all the variables of...

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Detalles Bibliográficos
Autores principales: Ivaskevics, Krisztián, Haller, József
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9133933/
https://www.ncbi.nlm.nih.gov/pubmed/35645939
http://dx.doi.org/10.3389/fpsyg.2022.745608
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author Ivaskevics, Krisztián
Haller, József
author_facet Ivaskevics, Krisztián
Haller, József
author_sort Ivaskevics, Krisztián
collection PubMed
description Hypothesis-driven approaches identified important characteristics that differentiate violent from non-violent radicals. However, they produced a mosaic of explanations as they investigated a restricted number of preselected variables. Here we analyzed without a priory assumption all the variables of the “Profiles of Individual Radicalization in the United States” database by a machine learning approach. Out of the 79 variables considered, 19 proved critical, and predicted the emergence of violence with an accuracy of 86.3%. Typically, violent extremists came from criminal but not radical backgrounds and were radicalized in late stages of their life. They were followers in terrorist groups, sought training, and were radicalized by social media. They belonged to low social strata and had problematic social relations. By contrast, non-violent but still criminal extremists were characterized by a family tradition of radicalism without having criminal backgrounds, belonged to higher social strata, were leaders in terrorist organizations, and backed terrorism by supporting activities. Violence was also promoted by anti-gay, Sunni Islam and Far Right, and hindered by Far Left, Anti-abortion, Animal Rights and Environment ideologies. Critical characteristics were used to elaborate a risk-matrix, which may be used to predict violence risk at individual level.
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spelling pubmed-91339332022-05-27 Risk Matrix for Violent Radicalization: A Machine Learning Approach Ivaskevics, Krisztián Haller, József Front Psychol Psychology Hypothesis-driven approaches identified important characteristics that differentiate violent from non-violent radicals. However, they produced a mosaic of explanations as they investigated a restricted number of preselected variables. Here we analyzed without a priory assumption all the variables of the “Profiles of Individual Radicalization in the United States” database by a machine learning approach. Out of the 79 variables considered, 19 proved critical, and predicted the emergence of violence with an accuracy of 86.3%. Typically, violent extremists came from criminal but not radical backgrounds and were radicalized in late stages of their life. They were followers in terrorist groups, sought training, and were radicalized by social media. They belonged to low social strata and had problematic social relations. By contrast, non-violent but still criminal extremists were characterized by a family tradition of radicalism without having criminal backgrounds, belonged to higher social strata, were leaders in terrorist organizations, and backed terrorism by supporting activities. Violence was also promoted by anti-gay, Sunni Islam and Far Right, and hindered by Far Left, Anti-abortion, Animal Rights and Environment ideologies. Critical characteristics were used to elaborate a risk-matrix, which may be used to predict violence risk at individual level. Frontiers Media S.A. 2022-05-12 /pmc/articles/PMC9133933/ /pubmed/35645939 http://dx.doi.org/10.3389/fpsyg.2022.745608 Text en Copyright © 2022 Ivaskevics and Haller. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Ivaskevics, Krisztián
Haller, József
Risk Matrix for Violent Radicalization: A Machine Learning Approach
title Risk Matrix for Violent Radicalization: A Machine Learning Approach
title_full Risk Matrix for Violent Radicalization: A Machine Learning Approach
title_fullStr Risk Matrix for Violent Radicalization: A Machine Learning Approach
title_full_unstemmed Risk Matrix for Violent Radicalization: A Machine Learning Approach
title_short Risk Matrix for Violent Radicalization: A Machine Learning Approach
title_sort risk matrix for violent radicalization: a machine learning approach
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9133933/
https://www.ncbi.nlm.nih.gov/pubmed/35645939
http://dx.doi.org/10.3389/fpsyg.2022.745608
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