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Detecting jihadist messages on twitter
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. (Security)
Univ Vienna, VORTEX, Vienna, Austria.
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, Computing Science. (Security)
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2015 (English)In: Proc. 5th European Intelligence and Security Informatics Conference, IEEE Computer Society, 2015, 161-164 p.Conference paper, Published paper (Refereed)
Abstract [en]

Jihadist groups such as ISIS are spreading online propaganda using various forms of social media such as Twitter and YouTube. One of the most common approaches to stop these groups is to suspend accounts that spread propaganda when they are discovered. This approach requires that human analysts manually read and analyze an enormous amount of information on social media. In this work we make a first attempt to automatically detect messages released by jihadist groups on Twitter. We use a machine learning approach that classifies a tweet as containing material that is supporting jihadists groups or not. Even tough our results are preliminary and more tests needs to be carried out we believe that results indicate that an automated approach to aid analysts in their work with detecting radical content on social media is a promising way forward. It should be noted that an automatic approach to detect radical content should only be used as a support tool for human analysts in their work.

Place, publisher, year, edition, pages
IEEE Computer Society, 2015. 161-164 p.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:uu:diva-272203DOI: 10.1109/EISIC.2015.27ISI: 000380550100027ISBN: 9781479986514 (print)OAI: oai:DiVA.org:uu-272203DiVA: diva2:893467
Conference
EISIC 2015, September 7–9, Manchester, UK
Available from: 2015-09-09 Created: 2016-01-12 Last updated: 2016-09-15Bibliographically approved

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Ashcroft, MichaelKaati, Lisa

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Citation style
  • apa
  • ieee
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  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
  • html
  • text
  • asciidoc
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