Citation

Altay A, Baykal-Gürsoy M, Hemmer P. Terrorism Polit. Violence 2022; 34(7): 1386-1414.

Copyright

(Copyright © 2022, Informa - Taylor and Francis Group)

DOI

10.1080/09546553.2020.1787384

PMID

unavailable

Abstract

Terrorist attacks carried out by individuals have significantly accelerated over the last twenty years. This type of lone-actor (LA) terrorism stands as one of the greatest security threats of our time. While the research on LA behavior and characteristics has produced valuable information on demographics, classifications, and warning signs, the relationship among these characteristics is yet to be addressed. Moreover, the means of radicalization and attacking have changed over decades. This study conducts an a-posteriori analysis of the temporal changes in LA terrorism and behavioral associations in LAs. We initially identify twenty-five binary behavioral characteristics of LAs and analyze 190 LAs. Next, we classify LAs according to ideology first, incident-scene behavior (determined via a virtual attacker-defender game) secondly, and, finally, the clusters obtained from the data. In addition, within each class, statistically significant associations and temporal relations are extracted using the A-priori algorithm. These associations would be instrumental in identifying the attacker's type and intervening at the right time. The results indicate that while pre-9/11 LAs were mostly radicalized by the people in their environment, post-9/11 LAs are more diverse. Furthermore, association chains for different LA types present unique characteristic pathways to violence and after-attack behavior.

Language: en

Keywords

A-priori algorithm; association rule mining; Lone-actor terrorism; R-rules; temporal associations