A Multi-level Approach to the Study of Violent Extremism

• NCJ Number: 305697
• Author(s): Michael Jensen; Gary LaFree
• Date Published: 2022
• Length: 122 pages
• Grant Number(s): 2017-ZA-CX-0001
• Agencies: NIJ-Sponsored

Annotation

With the goal of improving the risk analyses of violent extremist behaviors, the authors mapped co-offender networks present in a database, and used those data to construct a new dataset called the Social Networks of American Radicals (SoNAR), which would indicate changes in social network dynamics.

Abstract

The purpose of this study was to improve the validity, reliability, and utility of risk analyses of violent extremism by creating a relational database that combines individual-level radicalization risk factors with variables at the meso- and macro-levels of analysis. To accomplish this, the authors mapped co-offender networks present in a database called Profiles of Individual Radicalization in the United States (PIRUS), and used them to construct a new dataset called the Social Networks of American Radicals (SoNAR). Data from SoNAR indicated that social network dynamics have changed in recent years and U.S. extremists more often offend alone or as members of isolated cliques. The authors suggest that this has implications for the nature of radicalization among U.S. extremists since members of large networks are less likely to radicalize to violence than offenders who are members of isolated cliques or act alone. The authors demonstrate those dynamics by discussing U.S. extremists inspired by the Islamic State of Iraq and Syria (ISIS), and offenders from the modern militia movement such as the Oath Keepers, Three Percenters, and Boogaloo Movement. The authors suggest that local network dynamics played a critical intervening role in the decision-making of extremists in the U.S. and impacted radicalization outcomes. Researchers did not find that community-level pressures influenced individual-level radicalization to violence, however individual risk factors for violence such as gender (male), age (young), previous criminality, low socio-economic status, and mental health disorders, were robust predictors of violence. The authors conclude that the extremist offender landscape will continue to become less centralized and more loosely connected and that radicalization to violence will become more common. They recommend looking beyond individual-level vulnerabilities and considering dynamics of extremist relationships and how social connections in online and offline spaces may accelerate or moderate an individual’s pathway to violence.