Mental Health, Well-Being, and Adolescent Extremism: A Machine Learning Study on Risk and Protective Factors.

We examined the relationship between adolescents' extremist attitudes with a multitude of mental health, well-being, psycho-social, environmental, and lifestyle variables, using state-of-the-art machine learning procedure and nationally representative survey dataset of Norwegian adolescents (N = 11,397). Three key research questions were addressed: 1) can adolescents with extremist attitudes be distinguished from those without, using psycho-socio-environmental survey items, 2) what are the most important predictors of adolescents' extremist attitudes, and 3) whether the identified predictors correspond to specific latent factorial structures? Of the total sample, 17.6% showed elevated levels of extremist attitudes. The prevalence was significantly higher among boys and younger adolescents than girls and older adolescents, respectively. The machine learning model reached an AUC of 76.7%, with an equal sensitivity and specificity of 70.5% in the test dataset, demonstrating a satisfactory performance for the model. Items reflecting on positive parenting, quality of relationships with parents and peers, externalizing behavior, and well-being emerged as significant predictors of extremism. Exploratory factor analysis partially supported the suggested latent clusters. Out of the 550 psycho-socio-environmental variables analyzed, behavioral problems, individual and social well-being, along with basic needs such as a secure family environment and interpersonal relationships with parents and peers emerged as significant factors contributing to susceptibility to extremism among adolescents.