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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 |Volume: 11 |Issue: 4 |Pages: 19
ISSN: 1947-3435|EISSN: 1947-3443|EISBN13: 9781799861799|DOI: 10.4018/IJCWT.2021100105
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MLA

Al-Saggaf, Yeslam, and Patrick F. Walsh. "Islamists vs. Far Right Extremists: Insights Derived From Data Mining." IJCWT vol.11, no.4 2021: pp.74-92. http://doi.org/10.4018/IJCWT.2021100105

APA

Al-Saggaf, Y. & Walsh, P. F. (2021). Islamists vs. Far Right Extremists: Insights Derived From Data Mining. International Journal of Cyber Warfare and Terrorism (IJCWT), 11(4), 74-92. http://doi.org/10.4018/IJCWT.2021100105

Chicago

Al-Saggaf, Yeslam, and Patrick F. Walsh. "Islamists vs. Far Right Extremists: Insights Derived From Data Mining," International Journal of Cyber Warfare and Terrorism (IJCWT) 11, no.4: 74-92. http://doi.org/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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