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This paper, part of the Legal Perspectives on Tech Series, was commissioned in conjunction with the Congressional Counterterrorism Caucus.

While the threat that al-Shabaab poses to the West can easily be overstated, its outreach to Muslims living in Europe and the United States has been successful relative to other al-Qaeda-linked groups and warrants exploration. The organisation has recruited dozens of foreign fighters from the West (see Appendix). It also holds the dubious distinction of being the first jihadist organisation to recruit an American citizen to commit an act of suicide terrorism. Its recruitment strategy is therefore worthy of examination as a case study of how jihadist groups formulate strategies to lure Western Muslims. Through a combination of primary source analysis, background interviews in East Africa and an in-depth quantitative analysis of the group’s Twitter output, this paper aims to go beyond the simple statement of this problem by explaining how al-Shabaab markets itself to Muslims beyond its borders and what methods it employs. It also explores how the group is using social media to engage its followers in ways that other actors in the global jihad movement have not yet mastered.

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.

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.

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.

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