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Identifying Warning Behaviors of Violent Lone Offenders in Written Communication
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems. (Programkontoret Internet of Things)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
Washington Univ, St Louis, MO USA..
2016 (English)In: 2016 IEEE 16Th International Conference On Data Mining Workshops (ICDMW) / [ed] Domeniconi, C Gullo, F Bonchi, F DomingoFerrer, J BaezaYates, R Zhou, ZH Wu, X, New York: IEEE, 2016, 1053-1060 p.Conference paper, Published paper (Refereed)
Abstract [en]

Violent lone offenders such as school shooters and lone actor terrorists pose a threat to the modern society but since they act alone or with minimal help form others they are very difficult to detect. Previous research has shown that violent lone offenders show signs of certain psychological warning behaviors that can be viewed as indicators of an increasing or accelerating risk of committing targeted violence. In this work, we use a machine learning approach to identify potential violent lone offenders based on their written communication. The aim of this work is to capture psychological warning behaviors in written text and identify texts written by violent lone offenders. We use a set of features that are psychologically meaningful based on the different categories in the text analysis tool Linguistic Inquiry and Word Count (LIWC). Our study only contains a small number of known perpetrators and their written communication but the results are promising and there are many interesting directions for future work in this area.

Place, publisher, year, edition, pages
New York: IEEE, 2016. 1053-1060 p.
Series
International Conference on Data Mining Workshops, ISSN 2375-9232
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:uu:diva-332915DOI: 10.1109/ICDMW.2016.0152ISI: 000401906900144ISBN: 978-1-5090-5910-2 (electronic)OAI: oai:DiVA.org:uu-332915DiVA: diva2:1154564
Conference
16th IEEE International Conference on Data Mining (ICDM), DEC 12-15, 2016, Barcelona, SPAIN
Available from: 2017-11-02 Created: 2017-11-02 Last updated: 2017-11-02Bibliographically approved

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Kaati, LisaShrestha, Amendra

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