Comparing classification trees to discern patterns of terrorism* - Saiya, Nilay (2021)
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 fournovelclassiﬁcationmodelcomparisontechniquesfortheC4.5classiﬁcationmethodandapplies 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.