Islamists vs. Far Right Extremists: Insights Derived From Data Mining

Yeslam Al‐Saggaf, Patrick F. Walsh
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Abstract

In this study, a data mining technique, specifically a decision tree, was applied to look at the similarities and differences between Islamists and Far Right extremists in the Profiles of Individual Radicalisation in the United States (PIRUS) dataset. The aim was to identify differences and similarities across various groups that may highlight overlaps and variations across both Islamists and Far Right extremists. The data mining technique analysed data in the PIRUS dataset according to the PIRUS codebook's grouping of variables. The decision tree technique generated a number of rules that provided insights about previously unknown similarities and differences between Islamists and Far Right extremists. This study demonstrates that data mining is a valuable approach for shedding light on factors and patterns related to different forms of violent extremism.
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伊斯兰主义者与极右翼极端分子:来自数据挖掘的见解
在这项研究中,一种数据挖掘技术,特别是决策树,被应用于研究美国个人激进化概况(PIRUS)数据集中伊斯兰主义者和极右翼极端分子之间的异同。其目的是找出不同群体之间的差异和相似之处,这些差异可能会突出伊斯兰主义者和极右翼极端分子之间的重叠和差异。数据挖掘技术根据PIRUS码本的变量分组对PIRUS数据集中的数据进行分析。决策树技术产生了许多规则,这些规则提供了关于伊斯兰主义者和极右翼极端分子之间以前未知的异同的见解。这项研究表明,数据挖掘是揭示与不同形式的暴力极端主义有关的因素和模式的一种有价值的方法。
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来源期刊
CiteScore
1.80
自引率
40.00%
发文量
20
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