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With the exponential development of the virtual environment and social networks, radical elements have managed to promote and disseminate their radical ideas, in less time, to a wider and more diversified audience than in the past. This aspect has caused a shift of radicalization processes from the offline environment to the online environment, turning online radicalization into one of the biggest security challenges. This paper aims to (1) provide an insight into the hypothesis according to which the live-streaming of terrorist attacks in recent years could become a new radicalization mechanism, perhaps generating a new trend and (2) identify the elements and dynamics that determine attackers to use live-stream platforms at the time of the terrorist attack. The article proposes an analysis of the current theoretical framework that combines the brief analysis of the main models of online radicalization, highlighting the elements that could underlie a new radicalization model focused on live-stream/copycat terrorist attacks. In this article we aim to answer the following research question: can live-streaming represent a new mechanism of radicalization?

This literature review seeks to reorient the discourse on radicalization to consider the connection between communication technology and violent extremism. By interrogating three central questions vexing policy-makers, law enforcement officials and academics, this review moves away from a monolithic understanding of the internet and showcases the opportunities afforded by different communications technologies within the context of radicalization and recruitment. As this discussion shows, there is a consensus that despite significant exceptions to the rule, the internet alone does not act as a radicalizing agent, but rather serves as a facilitator and catalyst for terrorist organizations and their respective networks. Despite varying analyses produced within the literature, there is agreement that the virtual sphere does not replace the real world in most instances. Above all, a review of the current literature demonstrates that to answer the crucial questions posed in this article, more empirically-based research is required. This article is a revised and updated version of the 2017 VOX-Pol report Research Perspectives on Online Radicalisation: A Literature Review 2006 to 2016.

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Without measures of the lethality of terrorist networks, it is very difficult to assess if capturing or killing a terrorist is effective. We present the predictive lethality analysis of terrorist organization () algorithm, which merges machine learning with techniques from graph theory and social network analysis to predict the number of attacks that a terrorist network will carry out based on a network structure alone. We show that is highly accurate on two novel datasets, which cover Al Qaeda (AQ) and the Islamic State (ISIS). Using both machine learning and statistical methods, we show that the most significant macrofeatures for predicting AQ’s lethality are related to their public communications (PCs) and logistical subnetworks, while the leadership and operational subnetworks are most impactful for predicting ISISs lethality. Across both groups, the average degree and the diameters of the strongly connected components (SCCs) within these networks are strongly linked with lethality.

The permanent transition to online activity has brought with it a surge in hate speech discourse. This has prompted increased calls for automatic detection methods, most of which currently rely on a dictionary of hate speech words, and supervised classification. This approach often falls short when dealing with newer words and phrases produced by online extremist communities. These code words are used with the aim of evading automatic detection by systems. Code words are frequently used and have benign meanings in regular discourse, for instance, “skypes, googles, bing, yahoos” are all examples of words that have a hidden hate speech meaning. Such overlap presents a challenge to the traditional keyword approach of collecting data that is specific to hate speech. In this work, we first introduced a word embedding model that learns the hidden hate speech meaning of words. With this insight on code words, we developed a classifier that leverages linguistic patterns to reduce the impact of individual words. The proposed method was evaluated across three different datasets to test its generalizability. The empirical results show that the linguistic patterns approach outperforms the baselines and enables further analysis on hate speech expressions.

This paper combines corpus linguistics and critical discourse analysis methodologies in order to investigate the discourses and cyber activism of the British right-wing nationalist party, Britain First. A study of a corpus of texts produced by elite members of the group reveals a racist, xenophobic stance which constructs Islam and Muslims as the radical, dangerous ‘Other’. This creates a discourse of fear that threatens the way of life of the indigenous in-group of the British people. An investigation of the cyber activity of the group demonstrates that Britain First is able to achieve a significant amount of following on social media by publishing populist material that veils their true nature or ideological stance.