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Fernandez, Miriam; Asif, Moizzah and Alani, Harith
(2018).
DOI: https://doi.org/10.1145/3201064.3201082
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.
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About
- Item ORO ID
- 54344
- Item Type
- Conference or Workshop Item
- ISBN
- 1-4503-5563-3, 978-1-4503-5563-6
- Project Funding Details
-
Funded Project Name Project ID Funding Body Trivalent 740934 H2020 - Keywords
- Online Radicalisation; Radicalisation Influence; Counter-terrorism
- Academic Unit or School
-
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM) - Copyright Holders
- © 2018 The Authors
- Depositing User
- Miriam Fernandez