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

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

Yeslam Al-Saggaf, Patrick F. Walsh
Copyright: © 2021 |Pages: 19
DOI: 10.4018/IJCWT.2021100105
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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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The Data Mining Approach

Data mining techniques can discover hidden, useful and interesting rules (i.e., logic rules) in a given dataset (Al-Saggaf, 2020). Data mining techniques can discover patterns and trends in the data with confidence. This paper shows how a data mining technique can be applied to the PIRUS dataset to reveal patterns that separate Islamists from Far Rights from a number of perspectives. Interesting and previously unknown patterns about Islamists and Far Rights will be revealed using a decision tree algorithm.

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