Citation

Warren JI, Leviton ACR, Saathoff GB, Grabowska AA, Kiefner S, Alam MF, Fancher A, Patterson T. J. Threat Assess. Manag. 2020; 7(1-2): 41-71.

Copyright

(Copyright © 2020, American Psychological Association)

DOI

10.1037/tam0000148

PMID

unavailable

Abstract

The intelligence community faces a complex and everchanging task in the monitoring of at-risk individuals who pose potential threats to national security. Recently, the National Academies of Sciences, Engineering, and Medicine (2019) published a decadal survey that emphasized the vital role the social and behavioral sciences play in assisting the IC in answering emergent intelligence questions and in developing human-machine interactions capable of enhancing that capability. In this empirical study, we explore a theoretically based violence risk model, the moral-situational action of extremist violence (MSA-EV), to determine its usefulness in identifying women who have become involved in direct action related to violent extremism. The MSA-EV risk model contains 3 domains: propensity, mobilization, and capacity-building. Each domain was coded quantitatively and qualitatively using a 41-p. protocol that captures open source online material from both the surface web (i.e., the part of the Internet that is accessible to everyone) and the dark web (i.e, the part of the Internet invisible to search engines and that requires an anonymizing browser [Tor] to be accessed). Using logistic regression analyses, the model classified 300 individuals into either a high-risk category or a combined medium-risk category/low-risk category. When relevant variables from all 3 domains were plotted in terms of specificity and sensitivity, the corresponding area under the receiver operating characteristic curve was.90, which is indicative of excellent classification accuracy. These findings should be further validated in future research using an updated version of the coding manual and replicated with male individuals. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

Language: en