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Multi-lingual Detection of Terrorist Content on the Web

  • Mark Last
  • Alex Markov
  • Abraham Kandel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3917)

Abstract

Since the web is increasingly used by terrorist organizations for propaganda, disinformation, and other purposes, the ability to automatically detect terrorist-related content in multiple languages can be extremely useful. In this paper we describe a new, classification-based approach to multi-lingual detection of terrorist documents. The proposed approach builds upon the recently developed graph-based web document representation model combined with the popular C4.5 decision-tree classification algorithm. Evaluation is performed on a collection of 648 web documents in Arabic language. The results demonstrate that documents downloaded from several known terrorist sites can be reliably discriminated from the content of Arabic news reports using a simple decision tree.

Keywords

Machine Translation Arabic Language Document Representation Terrorist Content Simple Decision Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mark Last
    • 1
  • Alex Markov
    • 1
  • Abraham Kandel
    • 2
  1. 1.Department of Information Systems EngineeringBen-Gurion University of the NegevBeer-ShevaIsrael
  2. 2.Department of Computer Science and EngineeringUniversity of South FloridaTampaUSA

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