2019/04/17 by Nilay Saiya, Anthony Scime · 11 citations
Social Sciences · #Algorithm #Computer science #Crime Patterns and Interventions #Data science #Law #Political Conflict and Governance #Political science #Politics #Social science #Sociology #State (computer science) #Terrorism #Terrorism, Counterterrorism, and Political Violence
paper · doi:10.1111/ssqu.12629
published in Social Science Quarterly 100(4), 1420-1444 (Wiley)
openalex publication_date 2019/04/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
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.