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Understanding the Roots of Radicalisation on Twitter

Fernandez, Miriam; Asif, Moizzah and Alani, Harith (2018). Understanding the Roots of Radicalisation on Twitter. In: In WebSci ’18: 10th ACM Conference on Web Science, 27-30 May 2018, Amsterdam, Netherlands, ACM (Association for Computing Machinery).

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DOI (Digital Object Identifier) Link: https://doi.org/10.1145/3201064.3201082
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Abstract

In an increasingly digital world, identifying signs of online extremism sits at the top of the priority list for counter-extremist agencies. Researchers and governments are investing in the creation of advanced information technologies to identify and counter extremism through intelligent large-scale analysis of online data. However, to the best of our knowledge, these technologies are neither based on, nor do they take advantage of, the existing theories and studies of radicalisation. In this paper we propose a computational approach for detecting and predicting the radicalisation influence a user is exposed to, grounded on the notion of ’roots of radicalisation’ from social science models. This approach has been applied to analyse and compare the radicalisation level of 112 pro-ISIS vs.112 “general" Twitter users. Our results show the effectiveness of our proposed algorithms in detecting and predicting radicalisation influence, obtaining up to 0.9 F-1 measure for detection and between 0.7 and 0.8 precision for prediction. While this is an initial attempt towards the effective combination of social and computational perspectives, more work is needed to bridge these disciplines, and to build on their strengths to target the problem of online radicalisation.

Item Type: Conference or Workshop Item
Copyright Holders: 2018 The Authors
ISBN: 1-4503-5563-3, 978-1-4503-5563-6
Project Funding Details:
Funded Project NameProject IDFunding Body
Trivalent740934H2020
Keywords: Online Radicalisation; Radicalisation Influence; Counter-terrorism
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 54344
Depositing User: Miriam Fernandez
Date Deposited: 25 Apr 2018 15:38
Last Modified: 10 Sep 2018 17:47
URI: http://oro.open.ac.uk/id/eprint/54344
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