Classification of radical web text using a composite-based method

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Abstract

The spread of terrorism and extremism activities on the Internet has created the need for intelligence gathering via Web and real-time monitoring of potential websites for extremist activities. However, the manual classification for such contents is practically difficult and time-consuming. In response to this challenge, an automated classification system called Composite technique was developed. This is a computational framework that explores the combination of both semantics and syntactic features of textual contents of a Web page. We implemented the framework on a set of extremist Web pages - a dataset that has been subjected to a manual classification process. Thereby, we developed a classification model on the data using the J48 decision algorithm, to generate a measure of how well each page can be classified into their appropriate classes. The classification result obtained from our method when compared with other states of the art, indicated a 96% success rate overall in classifying Web pages when matched against the manual classification.
Original languageEnglish
Number of pages7
Publication statusPublished - 13 Dec 2018
EventIEEE International Conference on Computational Science and Computational Intelligence - Las Vegas, United States
Duration: 13 Dec 201815 Dec 2018
https://americancse.org/events/csci2018/Symposiums

Conference

ConferenceIEEE International Conference on Computational Science and Computational Intelligence
CountryUnited States
CityLas Vegas
Period13/12/1815/12/18
Internet address

Keywords

  • extremist
  • posit
  • classification
  • sentiment
  • web pages
  • composite

Cite this

Owoeye, K. O., & Weir, G. R. S. (2018). Classification of radical web text using a composite-based method. Paper presented at IEEE International Conference on Computational Science and Computational Intelligence, Las Vegas, United States.