Abstract
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.
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 language | English |
|---|---|
| DOIs | |
| Publication status | Published - 2018 |
| Event | ACM Conference on Web Science - Amsterdam, Netherlands Duration: 27 May 2018 → 30 May 2018 Conference number: 10 https://dl.acm.org/doi/proceedings/10.1145/3201064 |
Conference
| Conference | ACM Conference on Web Science |
|---|---|
| Country/Territory | Netherlands |
| Period | 27/05/18 → 30/05/18 |
| Internet address |
