University of Oulu

Oussalah M., Faroughian F., Kostakos P. (2018) On Detecting Online Radicalization Using Natural Language Processing. In: Yin H., Camacho D., Novais P., Tallón-Ballesteros A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science, vol 11315. Springer, Cham

On detecting online radicalization using natural language processing

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Author: Oussalah, Mourad1; Faroughian, F.2; Kostakos, Panos1
Organizations: 1Centre for Ubiquitous Computing, University of Oulu, Oulu, Finland
2Aston University, Aston, UK
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
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Language: English
Published: Springer Nature, 2018
Publish Date: 2020-04-21


This paper suggests a new approach for radicalization detection using natural language processing techniques. Although, intuitively speaking, detection of radicalization from only language cues is not trivial and very debatable, the advances in computational linguistics together with the availability of large corpus that allows application of machine learning techniques opens us new horizons in the field. This paper advocates a two stage detection approach where in the first phase a radicalization score is obtained by analyzing mainly inherent characteristics of negative sentiment. In the second phase, a machine learning approach based on hybrid KNN-SVM and a variety of features, which include 1, 2 and 3-g, personality traits, emotions, as well as other linguistic and network related features were employed. The approach is validated using both Twitter and Tumblr dataset.

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Series: Lecture notes in computer science
ISSN: 0302-9743
ISSN-E: 1611-3349
ISSN-L: 0302-9743
ISBN Print: 978-3-030-03495-5
Pages: 21 - 27
DOI: 10.1007/978-3-030-03496-2_4
Host publication: Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018
Host publication editor: Camacho, D.
Novais, P.
Tallon-Ballesteros, A. J.
Yin, H.
Conference: International Conference on Intelligent Data Engineering and Automated Learning
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Copyright information: © Springer Nature Switzerland AG 2018. This is a post-peer-review, pre-copyedit version of an article published in Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. The final authenticated version is available online at: