Comparing Classification Trees to Discern Patterns of Terrorism.

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. [ABSTRACT FROM AUTHOR]

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