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Applying unsupervised machine learning to counterterrorism

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  • Raj Bridgelall

    (North Dakota State University)

Abstract

To advance the agenda in counterterrorism, this work demonstrates how analysts can combine unsupervised machine learning, exploratory data analysis, and statistical tests to discover features associated with different terrorist motives. A new empirical text mining method created a “motive” field in the Global Terrorism Database to enable associative relationship mining among features that characterize terrorist events. The methodology incorporated K-means co-clustering, three methods of non-linear projection, and two spatial association tests to reveal statistically significant relationships between terrorist motives, tactics, and targets. Planners and investigators can replicate the approach to distill knowledge from big datasets to help advance the state of the art in counterterrorism.

Suggested Citation

  • Raj Bridgelall, 2022. "Applying unsupervised machine learning to counterterrorism," Journal of Computational Social Science, Springer, vol. 5(2), pages 1099-1128, November.
    • Handle: RePEc:spr:jcsosc:v:5:y:2022:i:2:d:10.1007_s42001-022-00164-w
    DOI: 10.1007/s42001-022-00164-w
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    References listed on IDEAS

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    1. Erik Pruyt & Jan H. Kwakkel, 2014. "Radicalization under deep uncertainty: a multi-model exploration of activism, extremism, and terrorism," System Dynamics Review, System Dynamics Society, vol. 30(1-2), pages 1-28, January.
    2. Yilmaz Bayar & Marius Dan Gavriletea, 2018. "Peace, terrorism and economic growth in Middle East and North African countries," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(5), pages 2373-2392, September.
    3. Ahmed Aleroud & Aryya Gangopadhyay, 2018. "Multimode co-clustering for analyzing terrorist networks," Information Systems Frontiers, Springer, vol. 20(5), pages 1053-1074, October.
    4. M. Irfan Uddin & Nazir Zada & Furqan Aziz & Yousaf Saeed & Asim Zeb & Syed Atif Ali Shah & Mahmoud Ahmad Al-Khasawneh & Marwan Mahmoud, 2020. "Prediction of Future Terrorist Activities Using Deep Neural Networks," Complexity, Hindawi, vol. 2020, pages 1-16, April.
    5. Fangyu Ding & Quansheng Ge & Dong Jiang & Jingying Fu & Mengmeng Hao, 2017. "Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-11, June.
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