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A Semantic Graph-Based Approach for Radicalisation Detection on Social Media

Saif, Hassan; Dickinson, Thomas; Kastler, Leon; Fernandez, Miriam and Alani, Harith (2017). A Semantic Graph-Based Approach for Radicalisation Detection on Social Media. In: ESWC 2017: The Semantic Web - Proceedings, Part I, Lecture Notes in Computer Science, pp. 571–587.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1007/978-3-319-58068-5_35
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Abstract

From its start, the so-called Islamic State of Iraq and the Levant (ISIL/ISIS) has been successfully exploiting social media networks, most notoriously Twitter, to promote its propaganda and recruit new members, resulting in thousands of social media users adopting a pro-ISIS stance every year. Automatic identification of pro-ISIS users on social media has, thus, become the centre of interest for various governmental and research organisations. In this paper we propose a semantic graph-based approach for radicalisation detection on Twitter. Unlike previous works, which mainly rely on the lexical representation of the content published by Twitter users, our approach extracts and makes use of the underlying semantics of words exhibited by these users to identify their pro/anti-ISIS stances. Our results show that classifiers trained from semantic features outperform those trained from lexical, sentiment, topic and network features by 7.8% on average F1-measure.

Item Type: Conference or Workshop Item
Copyright Holders: 2017 Springer International Publishing AG
Project Funding Details:
Funded Project NameProject IDFunding Body
COMRADESNot SetEC (European Commission): FP(inc.Horizon2020, H2020, ERC)
TRIVALENTNot SetEC (European Commission): FP(inc.Horizon2020, H2020, ERC)
Keywords: radicalisation detection; semantics; feature engineering; Twitter
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Interdisciplinary Research Centre: Centre for Policing Research and Learning (CPRL)
Centre for Research in Computing (CRC)
Related URLs:
Item ID: 49640
Depositing User: Harith Alani
Date Deposited: 19 Jun 2017 14:42
Last Modified: 02 Oct 2017 12:43
URI: http://oro.open.ac.uk/id/eprint/49640
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