Online social media in the Syria conflict: Encompassing the extremes and the in-betweens

O’Callaghan, Derek; Prucha, Nico; Greene, Derek; Conway, Maura; Carthy, Joe; Cunningham, Pádraig

doi:10.1109/asonam.2014.6921619

Cited by 81 publications

(17 citation statements)

References 21 publications

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“…Starting from 512 radicalised Twitter accounts, manually identified in the work of O'Callagan [26], they collected their followers, filtered those based in Europe and determined whether those followers were radicalised based on two hypothesis: (i) use of pro-ISIS terminology, a lexicon was generated to test this hypothesis, and (ii) content shared from pro-ISIS accounts. Their filtering process lead to the study of 727 pro-ISIS Twitter accounts and their complete timelines.…”

Section: Computational Approachesmentioning

confidence: 99%

Understanding the Roots of Radicalisation on Twitter

Fernández

Asif

Alani

2018

Proceedings of the 10th ACM Conference on Web Science

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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.

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Section: Computational Approachesmentioning

confidence: 99%

Understanding the Roots of Radicalisation on Twitter

Fernández

Asif

Alani

2018

Proceedings of the 10th ACM Conference on Web Science

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“…For instance, although O'Callaghan et al [11] did not necessary label Twitter users as being pro or anti-ISIS (as we do in this paper), the authors instead clustered Twitter users collected from Twitter lists related to the Syria conflict into high-modularity clusters. The authors subsequently identified a cluster of 'jihadist' users, which contained those who support ISIS.…”

Section: Related Workmentioning

confidence: 99%

“…11 This includes: number of followers, number of followee, number of hashtags, number of mentions (i.e., @user), favourites count, status count, profile description, and geographic location. The notion behind using these features for radicalisation detection is that users of a certain radicalisation stance are more likely to interact with other users of the same stance than with users from a different stance, as we discussed in our previous work [14].…”

Section: Baseline Featuresmentioning

confidence: 99%

A Semantic Graph-Based Approach for Radicalisation Detection on Social Media

Saif

Dickinson

Kastler

et al. 2017

Lecture Notes in Computer Science

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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 a 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 graph-based approach for radicalisation detection on Twitter. Unlike previous works, which mainly rely on the lexical 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 semantic features outperform those trained from lexical, sentiment, topic and network features by 7.8% on average F1-measure.

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“…For all other uses, contact the owner/author(s). sen by an expert panel as search queries; 2) collecting the random sample without specified search terms and extracting appropriate data [2]; 3) collecting from specific users that are known to be contributing to the debate [3].…”

Section: Introductionmentioning

confidence: 99%