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 proceedingConference contribution

8 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 languageEnglish (US)
Title of host publicationProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages2137-2146
Number of pages10
Volume2015-August
ISBN (Print)9781450336642
DOIs
StatePublished - Aug 10 2015
Event21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 - Sydney, Australia
Duration: Aug 10 2015Aug 13 2015

Other

Other21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015
CountryAustralia
CitySydney
Period8/10/158/13/15

Keywords

  • Causality
  • Rule learning
  • Security informatics

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    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, pp. 2137-2146). Association for Computing Machinery. https://doi.org/10.1145/2783258.2788591