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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. (Security)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems. (Security)
Washington University.
2016 (English)In: Proc. 16th ICDM Workshops, 2016Conference 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
2016.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:uu:diva-306943OAI: oai:DiVA.org:uu-306943DiVA: diva2:1044817
Conference
ICDM Workshop on Social Media and Risk, SOMERIS 2016, December 12, Barcelona, Spain
Available from: 2016-11-07 Created: 2016-11-07 Last updated: 2017-01-05Bibliographically approved

Open Access in DiVA

fulltext(146 kB)59 downloads
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Type fulltextMimetype application/pdf

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf