Mining for causal relationships: A data-driven study of the islamic state

Andrew Stanton; Amanda Thart; Ashish Jain; Priyank Vyas; Arpan Chatterjee; Paulo Shakarian

doi:10.1145/2783258.2788591

Mining for causal relationships: A data-driven study of the islamic state

Andrew Stanton, Amanda Thart, Ashish Jain, Priyank Vyas, Arpan Chatterjee, Paulo Shakarian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Scopus citations

Abstract

The Islamic State of Iraq and al-Sham (ISIS) is a dominant insurgent group operating in Iraq and Syria that rose to prominence when it took over Mosul in June, 2014. In this paper, we present a data-driven approach to analyzing this group using a dataset consisting of 2200 incidents of military activity surrounding ISIS and the forces that oppose it (including Iraqi, Syrian, and the American-led coalition). We combine ideas from logic programming and causal reasoning to mine for association rules for which we present evidence of causality. We present relationships that link ISIS vehicle-bourne improvised explosive device (VBIED) activity in Syria with military operations in Iraq, coalition air strikes, and ISIS IED activity, as well as rules that may serve as indicators of spikes in indirect fire, suicide attacks, and arrests.

Original language	English (US)
Title of host publication	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	2137-2146
Number of pages	10
Volume	2015-August
ISBN (Print)	9781450336642
DOIs	https://doi.org/10.1145/2783258.2788591
State	Published - Aug 10 2015
Event	21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 - Sydney, Australia Duration: Aug 10 2015 → Aug 13 2015

Other

Other	21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015
Country/Territory	Australia
City	Sydney
Period	8/10/15 → 8/13/15

Keywords

Causality
Rule learning
Security informatics

ASJC Scopus subject areas

Software
Information Systems

Access to Document

10.1145/2783258.2788591

Cite this

Mining for causal relationships: A data-driven study of the islamic state. / Stanton, Andrew; Thart, Amanda; Jain, Ashish et al.
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 2015-August Association for Computing Machinery, 2015. p. 2137-2146.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Stanton, A, Thart, A, Jain, A, Vyas, P, Chatterjee, A & Shakarian, P 2015, Mining for causal relationships: A data-driven study of the islamic state. in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. vol. 2015-August, Association for Computing Machinery, pp. 2137-2146, 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015, Sydney, Australia, 8/10/15. https://doi.org/10.1145/2783258.2788591

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AB - The Islamic State of Iraq and al-Sham (ISIS) is a dominant insurgent group operating in Iraq and Syria that rose to prominence when it took over Mosul in June, 2014. In this paper, we present a data-driven approach to analyzing this group using a dataset consisting of 2200 incidents of military activity surrounding ISIS and the forces that oppose it (including Iraqi, Syrian, and the American-led coalition). We combine ideas from logic programming and causal reasoning to mine for association rules for which we present evidence of causality. We present relationships that link ISIS vehicle-bourne improvised explosive device (VBIED) activity in Syria with military operations in Iraq, coalition air strikes, and ISIS IED activity, as well as rules that may serve as indicators of spikes in indirect fire, suicide attacks, and arrests.

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Mining for causal relationships: A data-driven study of the islamic state

Abstract

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Keywords

ASJC Scopus subject areas

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