Correspondence:
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Address for correspondence: Prof. Dr. Lieven Pauwels, Director IRCP- Institute for International Research on Criminal Policy Ghent University- Faculty of Law and Criminology, Department of Criminology, Criminal Law and Social Law, Belgium. Tel.: +32 0 9 264 68 37; E-mail: lieven.pauwels@ugent.be.
Abstract: In the present study, the interaction between specific measures of endorsement for extremism (e.g. endorsement for religious, left-wing or far-right extremism), thrill-seeking, and active online exposure to extremism via social media with regard to the explanation of politically and/or religiously motivated aggression is investigated. While the relationship between exposure to crime-prone contexts and aggression has been studied widely, no previous study has explicitly demonstrated the conditional effects of these factors in a survey of young adults and with regards to political aggression. This study therefore extends the existing literature by testing propositions derived from the General Aggression Model, a well-established theory of aggression. The unique contribution of this study is that it is based on distinctive measures of endorsement for extremism (left-wing, nationalist/separatist and religious extremism) and that it focuses on the differential effect of exposure to extremist content online. We make use of a large-scale web survey of young adults in Belgium. 6,020 respondents completed the online questionnaire. Mean age (range, 15– 31 years) was 20.19 years, 35.3% males. The results support an amplification effect: Endorsement for extremism is related to self-reported political aggression, but the effect of endorsement increases by levels of active social media exposure. The results additionally showed that the magnitude of this interaction effect is further depending on thrill-seeking. These findings are rather stable across extremism-specific measures of endorsement for extremism.
Keywords: Political aggression, thrill-seeking, endorsement for extremism, exposure to extremist content, General Aggression Model
