Comparing classification trees to discern patterns of terrorism*
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 algor...
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sg-ntu-dr.10356-1457382023-03-05T15:31:19Z Comparing classification trees to discern patterns of terrorism* Saiya, Nilay Scime, Anthony School of Social Sciences Social sciences Terrorism Classification Trees 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. Accepted version This research was supported by a Start‐Up Grant from Nanyang Technological University. 2021-01-06T08:03:11Z 2021-01-06T08:03:11Z 2019 Journal Article Saiya, N., & Scime, A. (2019). Comparing classification trees to discern patterns of terrorism*. Social Science Quarterly, 100(4), 1420-1444. doi:10.1111/ssqu.12629 0038-4941 https://hdl.handle.net/10356/145738 10.1111/ssqu.12629 4 100 1420 1444 en Social Science Quarterly This is the accepted version of the following article: Saiya, N., & Scime, A. (2019). Comparing classification trees to discern patterns of terrorism*. Social Science Quarterly, 100(4), 1420-1444. doi:10.1111/ssqu.12629, which has been published in final form at https://doi.org/10.1111/ssqu.12629. This article may be used for non-commercial purposes in accordance with the Wiley Self-Archiving Policy [https://authorservices.wiley.com/authorresources/Journal-Authors/licensing/self-archiving.html]. application/pdf |
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Social sciences Terrorism Classification Trees Saiya, Nilay Scime, Anthony Comparing classification trees to discern patterns of terrorism* |
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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. |
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School of Social Sciences |
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School of Social Sciences Saiya, Nilay Scime, Anthony |
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Saiya, Nilay Scime, Anthony |
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Saiya, Nilay |
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Comparing classification trees to discern patterns of terrorism* |
title_short |
Comparing classification trees to discern patterns of terrorism* |
title_full |
Comparing classification trees to discern patterns of terrorism* |
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Comparing classification trees to discern patterns of terrorism* |
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Comparing classification trees to discern patterns of terrorism* |
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comparing classification trees to discern patterns of terrorism* |
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2021 |
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https://hdl.handle.net/10356/145738 |
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