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This article explores why jihadis make propaganda. Through the analytical lens of Bockstette’s 2008 framework for jihadi communication strategies, it assesses two of the Islamic State’s most important doctrinal texts on media jihad—the first, a little-known speech by Abu Hamzah al-Muhajir that was published posthumously in 2010, and the second, a field-guide prepared by the Islamic State’s official publishing house, the Himmah Library, in 2015. After drawing out the core insights, similarities and presuppositions of each text, it discusses the enduring salience of Bockstette’s model on the one hand and these two texts on the other, noting that, while it is imprudent to make policy predictions based on them alone, so too would it be remiss to ignore the strategic insights they contain.

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.

It will have been 25 years since the Rwanda genocide in spring 2019. As more information about the Rwanda genocide becomes available and as the narrative of those events continues to evolve, there is still much to learn from the case study of Rwanda about the role of media in stimulating and responding to mass atrocities. In an era of social media saturation, near-ubiquitous mobile device penetration and dramatic shifts in traditional news media, it is more important than ever to examine the nexus between media and mass atrocity. Advances in information and communications technology have reshaped the media landscape, rendering mass atrocities in distant countries more immediate and harder to ignore. And yet, a cohesive international response to mass atrocities has been elusive. Social media tools can be used to inform and engage, but — in an echo of hate radio in Rwanda — can also be used to demonize opponents and mobilize extremism. With enhanced and relatively inexpensive communications technologies, ordinary citizens around the globe can capture live footage of human rights abuses before journalists have the chance, making social media itself a global actor, affecting the responses of national governments and international organizations to threats against peace and security and human rights. And yet, despite the extended reach that technological advances have afforded traditional news media and social media, the media impact in mass atrocity events is still a complex subject. Specifically, we are left with many troubling questions, still unresolved despite the passage of time since Rwanda. What role do media play in alerting the international community to looming mass atrocity? Could more informed and comprehensive coverage of mass atrocities mitigate or even halt the killing by sparking an international outcry? How do we assess the impact of hate media reporting in a killing spree? What is the role of the media in trying to encourage amelioration of the conflict or post-conflict reconciliation? What do the lessons of Rwanda mean now, in an age of communications so dramatically influenced by social media? Media and Mass Atrocity: The Rwanda Genocide and Beyond grapples with these questions.

Right-wing extremists and Islamist extremists try to recruit new followers by addressing their national (for instance, German) or religious (Muslim) social identity via online propaganda videos. Two studies examined whether capitalizing on a shared group-membership affects the emotional and cognitive response towards extremist propaganda. In both studies, Germans/non-migrants, Muslim migrants and control participants (N = 235) were confronted with right-wing extremist and Islamist extremist videos. Emotional and cognitive effects of students (Study 1) and apprentices (Study 2) were assessed. Results showed a general negative evaluation of extremist videos. More relevant, in-group propaganda led to more emotional costs in both studies. Yet, the responses varied depending on educational level: students reported more negative emotions and cognitions after in-group directed videos, while apprentices reported more positive emotions and cognitions after in-group directed propaganda. Results are discussed considering negative social identities.

As with so many technologies, the Internet’s racism was programmed right in—and it’s quickly fueled the spread of White supremacist, xenophobic rhetoric throughout the western world.

A new wave of growing antisemitism, driven by fringe Web communities, is an increasingly worrying presence in the socio-political realm. The ubiquitous and global nature of the Web has provided tools used by these groups to spread their ideology to the rest of the Internet. Although the study of antisemitism and hate is not new, the scale and rate of change of online data has impacted the efficacy of traditional approaches to measure and understand this worrying trend. In this paper, we present a large-scale, quantitative study of online antisemitism. We collect hundreds of million comments and images from alt-right Web communities like 4chan’s Politically Incorrect board (/pol/) and the Twitter clone, Gab. Using scientifically grounded methods, we quantify the escalation and spread of antisemitic memes and rhetoric across the Web. We find the frequency of antisemitic content greatly increases (in some cases more than doubling) after major political events such as the 2016 US Presidential Election and the “Unite the Right” rally in Charlottesville. Furthermore, this antisemitism appears in tandem with sharp increases in white ethnic nationalist content on the same communities. We extract semantic embeddings from our corpus of posts and demonstrate how automated techniques can discover and categorize the use of antisemitic terminology. We additionally examine the prevalence and spread of the antisemitic “Happy Merchant” meme, and in particular how these fringe communities influence its propagation to more mainstream services like Twitter and Reddit. Taken together, our results provide a data-driven, quantitative framework for understanding online antisemitism. Our open and scientifically grounded methods serve as a framework to augment current qualitative efforts by anti-hate groups, providing new insights into the growth and spread of antisemitism online.