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Terrorism research has begun to focus on the issue of radicalization, or the acceptance of ideological belief systems that lead toward violence. There has been particular attention paid to the role of the Internet in the exposure to and promotion of radical ideas. There is, however, minimal work that attempts to model the ways that messages are spread or how individual participation in radical on-line communities operates. In this paper, we present a stochastic linear system to represent the evolution of contribution to a sample of 126 threads in an on-line forum where individuals discuss radical belief systems. To estimate or predict the time-varying contributions of agents for given onlineforum data, each agent’s contribution has been modeled as a state variable. We then use the expectation-maximization (EM) algorithm to identify the model parameters including the adjacency matrix of the graph constructed among participating agents along with measurement and system uncertainty levels in online-postings. Our approach reveals the identified dynamical influences among agents in the time-varying shaping of the contribution in a datadriven fashion. We use the real-world data from online-postings to demonstrate the usefulness of our approach, and its application toward on-line radicalization.

Counter-narratives and campaigns promoting normality, are often highlighted as universal means against online propaganda from militant movements. However, such campaigns are driven by a number of unfortunate assumptions and are difficult to apply in practice.

This work examines the intersections of subcultural theories and radicalization theories from terrorism studies to identify how they may be improved through integration. To date there have been almost no efforts to merge these frameworks, though terrorism shares common characteristics of deviant subcultures. Both are driven by ideologies that are in opposition to that of their targets. We focus particularly on the process of online radicalization to assess how subcultural research in online environments may inform the process of enculturation into a terrorist belief system. We conclude by discussing the implications of this expansion for research on terrorism and subcultures.

Using a dataset of over 1.9 million messages posted on Twitter by about 25,000 ISIS members, we explore how ISIS makes use of social media to spread its propaganda and to recruit militants from the Arab world and across the globe. By distinguishing between violence-driven, theological, and sectarian content, we trace the connection between online rhetoric and key events on the ground. To the best of our knowledge, ours is one of the first studies to focus on Arabic content, while most literature focuses on English content. Our findings yield new important insights about how social media is used by radical militant groups to target the Arab-speaking world, and reveal important patterns in their propaganda efforts.

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 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-based approach for radicalisation detection on Twitter. Unlike most previous works, which mainly rely on the lexical and contextual 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 words’ semantics outperform those trained from lexical and network features by 2% on average F1-measure.