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With the beheading video of U.S. photojournalist James Foley, the Islamic State (IS) initiated a hostage video campaign that received tremendous coverage in the international news media. This backgrounder highlights the most important aspects of IS hostage videos with a particular focus on their media strategic functions, which, in part, stand in sharp contrast with strategic guiding principles voiced by traditional Jihadist ideologue such as Ayman al-Zawahiri – exposing the growing rift between the IS and Al-Qaeda. Providing examples from primary sources and identifying commonalities and differences to earlier hostage footage, the Research Note illustrates that the IS hostage video campaign is rationally calculated, multifaceted, and constantly changing.

This chapter is a contribution to the debate on information resilience and national security from the perspective of countering violent Islamist extremism in the arc of instability. The narratives crafted by various violent Islamist extremists are a form of information resilience, but in this case, they act as counterforces, hindering state efforts to prepare for and withstand the onslaughts of terror groups. This challenge is particularly pronounced in weak and fragile states within the arc of instability. The aims of the chapter are threefold: to explore the information resilience of terrorist groups vying for power in the arc of instability; to examine the reactions and responses that extremism has produced in the commercial media; and, finally, to discuss the key challenges that governments are grappling with in controlling media content that is seen as having the potential to destabilise the already fragile security situation.

This report identifies and names the Alternative Influence Network (AIN): an assortment of scholars, media pundits, and internet celebrities who use YouTube to promote a range of political positions, from mainstream versions of libertarianism and conservatism, all the way to overt white nationalism. Content creators in the AIN
claim to provide an alternative media source for news and political commentary. They function as political influencers who adopt the techniques of brand influencers to build audiences and “sell” them on far-right ideology. This report presents data from approximately 65 political influencers across 81 channels. This network is connected through a dense system of guest appearances, mixing content from a variety of ideologies. This cross-promotion of ideas forms a broader “reactionary” position: a general opposition to feminism, social justice, or left-wing politics.

This is an analysis of what the Alt-Right stands for and who its followers and leaders are. Including exclusive interviews with members of the movement and evidence linking extremists with terror attacks and hate crimes, it is clear that despite its high-profile support, the movement’s lack of a coherent base and its contradictory tendencies is already leading to its downfall.

The rise of the alt-right as a potent and sometimes violent political force has been well documented. Yet the journey of an individual towards upholding these ideologies is less well understood. Alt-righters are not instantly converted, but rather incrementally nudged along a particular medial pathway. Drawing on video testimonies, chat logs, and other studies, this paper explores the interaction between this alt-right “pipeline” and the psyche of a user. It suggests three overlapping cognitive phases that occur within this journey: normalization, acclimation, and dehumanization. Finally, the article examines the individual who has reached the end of this journey, an extremist who nevertheless remains largely unregistered within traditional terrorist classifications.

With the spread of social networks and their unfortunate use for hate speech, automatic detection of the latter has become a pressing problem. In this paper, we reproduce seven state-of-the-art hate speech detection models from prior work, and show that they perform well only when tested on the same type of data they were trained on. Based on these results, we argue that for successful hate speech detection, model architecture is less important than the type of data and labeling criteria. We further show that all proposed detection techniques are brittle against adversaries who can (automatically) insert typos, change word boundaries or add innocuous words to the original hate speech. A combination of these methods is also effective against Google Perspective – a cutting edge solution from industry. Our experiments demonstrate that adversarial training does not completely mitigate the attacks, and using character-level features makes the models systematically more attack-resistant than using word-level features.