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Yemeni-American cleric Anwar al-Awlaki, the leading English-language propagandist for al-Qa`ida, was killed in an American drone strike in 2011. But his influence has lived on into the Islamic State era, enhanced by his status as a martyr for Islam in the eyes of his admirers. His massive internet presence has turned up as a factor in several attacks since his death, including most recently the San Bernardino shootings and the Orlando nightclub attack as well as a significant number of terrorism cases on both sides of the Atlantic. Despite his long association with al-Qa`ida and that network’s rivalry with the upstart Islamic State, al-Awlaki has been embraced by the Islamic State and its followers, and he continues to inspire terrorism from beyond the grave.

Mubin Shaikh, Police Project Consultant with the Romeo Dallaire Initiative, discusses the evolution of radical propaganda online, social media and foreign fighters and the need for a robust counter narrative. Mr. Shaikh brings his real world knowledge of these issues as a former CSIS and RCMP operative, discussing them from the perspective of a real world and online counter-terrorism practitioner.

This interview is part of The SecDev Foundation’s Prevent Violent Extremism: A Social Media Research Portal – https://preventviolentextremism.info/

My thesis examines how metaphors of globalization shape the global governance of the Internet. I consider how, in a short span of time, discussions of the Internet’s globalizing potential have gone from the optimism of the global village to the penchant of the global marketplace to the anxiety of the global war on terror. Building upon Rorty’s theory of metaphors and Foucault’s notion of productive power, I investigate how the shifts in these prevailing metaphors have produced and legitimated different frameworks of global governance. In considering how these patterns of governance have been shaped in the context of a familiar example of globalization, I demonstrate that globalization has an important discursive dimension that works as a constitutive force – not only in Internet governance but in global governance more generally. By illuminating globalization’s discursive dimensions, this thesis makes an original theoretical contribution to the study of globalization and global governance. It demonstrates that globalization is more than a set of empirical flows: equally important, globalization exists as a set of discourses that reconstitute political legitimacy in more ‘global’ terms. This recasts the conventional understanding of global governance: rather than a response to the challenges posed by the empirical transcendence of territorial borders or the visible proliferation of non-state actors, the aims, institutions, and policies of global governance are shaped and enabled by discourses of globalization and evolve as these discourses change. In short, this thesis provides further insight into globalization’s transformations of state-based political order. It links these transformations to the discursive processes by which systems of global governance are produced and legitimated as sites of power and authority.

VOX-Pol Blog post.

This report provides an overview of two seemingly new formations of online networks of accelerationist and neo-fascist entities that emerged in early 2021 and are self-described Atomwaffen Division spinoffs. It also provides an understanding of how they behave online and speaks to the threats of offline violence they pose.

Common approaches to text categorization essentially rely either on n-gram counts or on word embeddings. This presents important difficulties in highly dynamic or quickly-interacting environments, where the appearance of new words and/or varied misspellings is the norm. A paradigmatic example of this situation is abusive online behavior, with social networks and media platforms struggling to effectively combat uncommon or nonblacklisted hate words. To better deal with these issues in those fast-paced environments, we propose using the error signal
of class-based language models as input to text classification algorithms. In particular, we train a next-character prediction model for any given class, and then exploit the error of such class-based models to inform a neural network classifier. This way, we shift from the ability to describe seen documents to the ability to predict unseen content. Preliminary studies using out-of-vocabulary splits from abusive tweet data show promising results, outperforming competitive text categorization strategies by 4–11%.