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) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe2020042119546 |
Language: | English |
Published: |
Springer Nature,
2018
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Publish Date: | 2020-04-21 |
Description: |
AbstractThis 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. see all
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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 |
OADOI: | https://oadoi.org/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 |
Subjects: | |
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: https://doi.org/10.1007/978-3-030-03496-2_4. |