Identifying the responsible group for extreme acts of violence through pattern recognition

Mahdi Hashemi, Margeret Hall

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The expansion of Internet has eased the broadcasting of data, information, and propaganda. The availability of myriads of social and televised media have turned the spotlight on violent extremism, widened the rift between different sides of the spectrum, and expanded the scope and impact of ideology-oriented acts of violence on citizens and nations. The human casualties and psychological impacts on societies make any study on such acts worthwhile, let alone attempting to detect patterns among them. This study focuses on mining the information about each violent act, including human casualties and fatalities, level of coordination and expertise, importance of the targeted process, and the extent of its impact on the process, to identify the responsible group. Decision tree, a non-linear classifier, reached 20% cross-validation accuracy in identifying the correct group among 38 groups. This is the highest accuracy achieved in comparison with other linear classifiers, including Perceptron, SVM, and least squares. Our results also underscored the human casualties and fatalities as the most important predictors. The other four variables, including level of coordination, level of expertise, importance of the targeted process, and the extent of the impact on the process were all partly correlated and less helpful. However, the single feature, generated by linear combination of these four features using PCA, was as good of a predictor as the human casualties and fatalities.

Original languageEnglish (US)
Title of host publicationHCI in Business, Government, and Organizations - 5th International Conference, HCIBGO 2018, Held as Part of HCI International 2018, Proceedings
EditorsBo Sophia Xiao, Fiona Fui-Hoon Nah
PublisherSpringer Verlag
Pages594-605
Number of pages12
ISBN (Print)9783319917153
DOIs
Publication statusPublished - Jan 1 2018
Event5th International Conference on HCI in Business, Government, and Organizations, HCIBGO 2018 Held as Part of HCI International 2018 - Las Vegas, United States
Duration: Jul 15 2018Jul 20 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10923 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Conference on HCI in Business, Government, and Organizations, HCIBGO 2018 Held as Part of HCI International 2018
CountryUnited States
CityLas Vegas
Period7/15/187/20/18

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Keywords

  • Decision tree
  • Feature selection
  • Least squares
  • Machine learning
  • Perceptron
  • SVM

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Hashemi, M., & Hall, M. (2018). Identifying the responsible group for extreme acts of violence through pattern recognition. In B. S. Xiao, & F. F-H. Nah (Eds.), HCI in Business, Government, and Organizations - 5th International Conference, HCIBGO 2018, Held as Part of HCI International 2018, Proceedings (pp. 594-605). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10923 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-91716-0_47