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Detecting multipliers of jihadism on twitter
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)
ICSR, London, England.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems. (Security)
2015 (English)In: Proc. 15th ICDM Workshops, IEEE Computer Society, 2015, 954-960 p.Conference paper, Published paper (Refereed)
Abstract [en]

Detecting terrorist related content on social media is a problem for law enforcement agency due to the large amount of information that is available. In this paper we describe a first step towards automatically classifying twitter user accounts (tweeps) as supporters of jihadist groups who disseminate propaganda content online. We use a machine learning approach with two set of features: data dependent features and data independent features. The data dependent features are features that are heavily influenced by the specific dataset while the data independent features are independent of the dataset and that can be used on other datasets with similar result. By using this approach we hope that our method can be used as a baseline to classify violent extremist content from different kind of sources since data dependent features from various domains can be added.

Place, publisher, year, edition, pages
IEEE Computer Society, 2015. 954-960 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:uu:diva-272243DOI: 10.1109/ICDMW.2015.9ISI: 000380556700127ISBN: 9781467384926 (print)OAI: oai:DiVA.org:uu-272243DiVA: diva2:893574
Conference
ICDM Workshop on Intelligence and Security Informatics, ISI-ICDM 2015, November 14, Atlantic City, NJ
Available from: 2015-11-14 Created: 2016-01-12 Last updated: 2016-09-15Bibliographically approved

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Kaati, LisaShrestha, Amendra

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Citation style
  • apa
  • ieee
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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