Governments face a tough and timeless challenge: Dealing with the capability of radical terrorist organizations to recruit foreign fighters. However, scholars so far have ignored that this phenomenon pertains to the realm of complexity theory, failing to determine the combination of country-level variables able to catalyze this issue. This is an important concern if countries want to design effective socio-po-litical strategies aimed at decreasing terrorist groups' capability to enroll foreign fighters or, at least, to curtail the penetration of their radical message. Thus, to address this issue we undertake an exploratory data mining approach (knowledge discovery in databases) to discover country-level patterns which might engender conditions that induce people to join an extremist organization, based on the case of ISIS. After a pre-selection procedure, the 950 variables initially selected were reduced to 22, and subsequently used in decision tree algorithms. Findings reveal the existence of six specific country clusters, which are characterized by some spatial, structural (economic and political), and social variables that create favor¬able conditions for the emergence of the phenomenon. Academic and practical recommendations are then discussed.
|Translated title of the contribution||Unraveling Spatial, Structural, and Social Country- Level Conditions for the Emergence of the Foreign Fighter Phenomenon: An Exploratory Data Mining Approach to the Case Of ISIS|
|State||Published - 2022|
- Complexity theory
- Data mining
- Foreign fighters
- Knowledge discovery in databases