Comparing Classification Trees to Discern Patterns of Terrorism

Nilay Saiya and Anthony Scime

Social Science Quarterly, 2019, vol. 100, issue 4, 1420-1444

Abstract: Objective Though applied widely in the fields of medicine, finance, ecology, psychology, and computer science, machine learning algorithmic‐based methods are a relatively novel approach to social scientific analysis that have yet to be extensively applied. Yet as we argue in this article, a specific form of algorithmic analysis known as C4.5 classification trees has much to offer social analysis and, specifically, the study of social and political violence. Method This article describes four novel classification model comparison techniques for the C4.5 classification method and applies them to the study of terrorism. Results Our state‐level analysis suggests that there is something fundamentally different in the targeting choices of religious and secular terrorists. Conclusion This analysis highlights the ability of classification trees to heighten our understanding of terrorism and even provide recommendations to policymakers for avoiding future attacks.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1111/ssqu.12629

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:socsci:v:100:y:2019:i:4:p:1420-1444

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0038-4941

Access Statistics for this article

Social Science Quarterly is currently edited by Robert L. Lineberry

More articles in Social Science Quarterly from Southwestern Social Science Association
Bibliographic data for series maintained by Wiley Content Delivery ().