Understanding the Roots of Radicalisation on Twitter

Miriam Fernandez, Moizzah Asif, Harith Alani

    Research output: Contribution to conferencePaperpeer-review


    In an increasingly digital world, identifying signs of online extremism sits at the top of the priority list for counter-extremist agencies.1
    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.
    Original languageEnglish
    Publication statusPublished - 2018
    EventACM Conference on Web Science - Amsterdam, Netherlands
    Duration: 27 May 201830 May 2018
    Conference number: 10


    ConferenceACM Conference on Web Science
    Internet address


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