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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Main Authors: Saiya, Nilay, Scime, Anthony
Other Authors: School of Social Sciences
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/145738
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Institution: Nanyang Technological University
Language: English
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spelling 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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Social sciences
Terrorism
Classification Trees
spellingShingle Social sciences
Terrorism
Classification Trees
Saiya, Nilay
Scime, Anthony
Comparing classification trees to discern patterns of terrorism*
description 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.
author2 School of Social Sciences
author_facet School of Social Sciences
Saiya, Nilay
Scime, Anthony
format Article
author Saiya, Nilay
Scime, Anthony
author_sort Saiya, Nilay
title 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*
title_fullStr Comparing classification trees to discern patterns of terrorism*
title_full_unstemmed Comparing classification trees to discern patterns of terrorism*
title_sort comparing classification trees to discern patterns of terrorism*
publishDate 2021
url https://hdl.handle.net/10356/145738
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