Saif, Hassan; Fernández, Miriam; Rowe, Matthew and Alani, Harith
(2016).
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| URL: | http://ceur-ws.org/Vol-1690/paper66.pdf |
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| Google Scholar: | Look up in Google Scholar |
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 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-based approach for radicalisation detection on Twitter. Unlike most previous works, which mainly rely on the lexical and contextual 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 words’ semantics outperform those trained from lexical and network features by 2% on average F1-measure.
| Item Type: | Conference or Workshop Item |
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| Copyright Holders: | 2016 The Authors |
| ISSN: | 1613-0073 |
| 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) |
| Research Group: | Centre for Research in Computing (CRC) |
| Item ID: | 48478 |
| Depositing User: | Kay Dave |
| Date Deposited: | 20 Feb 2017 16:14 |
| Last Modified: | 28 Mar 2018 09:59 |
| URI: | http://oro.open.ac.uk/id/eprint/48478 |
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