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Over the past decade, two major phenomena have developed in the digital realm. On the one hand, extremism has grown massively on the Internet, with sprawling online ecosystems hosting a wide range of radical subcultures and communities associated with both ‘stochastic terrorism’ and the ‘mainstreaming of extremism’. On the other hand, Artificial Intelligence (AI) has undergone exponential improvement: from ChatGPT to video deepfakes, from autonomous vehicles to face-recognition CCTV systems, an array of AI technologies has abruptly entered our everyday lives. This report examines ‘AI extremism’, the toxic encounter of these two evolutions – each worrying in its own right. Like past technological progress, AI will indeed be – in fact already is – used in various ways to bolster extremist agendas. Identifying the many opportunities for action that come with a range of AI models, and linking them with different types of extremist actors, we offer a clear overview of the numerous facets of AI extremism. Building on the nascent academic and government literature on the issue as well as on our own empirical and theoretical work, we provide new typologies and concepts to help us organize our understanding of AI extremism, systematically chart its instantiations, and highlight thinking points for stakeholders in countering violent extremism.

Anhand der Einzelfallstudie des Radikalisierungsprozesses einer jihadistischen Straftäterin möchte dieser Beitrag die besondere Rolle des Internets bei Radikalisierungen von Extremisten aufzeigen. Die Straftäterin war in den Jahren 2007 bis 2010 einePropagandistin, die über das Internet mehrere terroristische Vereinigungen unterstützte, indem sie deren Videobotschaften online verbreitete. In der Einzelfallstudie wird aufgezeigt, wie sich die Jihadistin durch virtuelle Kontakte, Propagandaaktivitäten sowie ihre Zusammenarbeit mit zahlreichen Jihadisten im Ausland über das Internet radikalisierte.

The Role of the Internet on Radicalization of a German Jihadist Offender: A Case Study
Based on the analysis of a case study this article raises the question about the role of the internet in accelerating the radicalization of a woman who joined the jihadist milieu in southern Germany in the mid 2000s. From 2007 to 2010 she was a jihadist activist on the internet who supported several terrorist groups by publishing videos and jihadi messages in blogs, video channels and web forums. The case study shows how the woman was radicalized through virtual contacts, interactions in virtual communities, as well as through her support of several jihadists abroad.

Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of the natural language constructs makes this task very challenging. We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity. Our experiments on a benchmark dataset of 16K annotated tweets show that such deep learning methods outperform state-of-the-art char/word n-gram methods by ~18 F1 points.

With the ever-increasing cases of hate spread on social media platforms, it is critical to design abuse detection mechanisms to pro-actively avoid and control such incidents. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. Bias removal has been traditionally studied for structured datasets, but we aim at bias mitigation from unstructured text data. In this paper, we make two important contributions. First, we systematically design methods to quantify the bias for any model and propose algorithms for identifying the set of words which the model stereotypes. Second, we propose novel methods leveraging knowledge-based generalizations for bias-free learning. Knowledge-based generalization provides an effective way to encode knowledge because the abstraction they provide not only generalizes content but also facilitates retraction of information from the hate speech detection classifier, thereby reducing the imbalance. We experiment with multiple knowledge generalization policies and analyze their effect on general performance and in mitigating bias. Our experiments with two real-world datasets, a Wikipedia Talk Pages dataset (WikiDetox) of size ~ 96k and a Twitter dataset of size ~ 24k, show that the use of knowledge-based generalizations results in better performance by forcing the classifier to learn from generalized content. Our methods utilize existing knowledge-bases and can easily be extended to other tasks.

Using a dataset of over 1.9 million messages posted on Twitter by about 25,000 ISIS members, we explore how ISIS makes use of social media to spread its propaganda and to recruit militants from the Arab world and across the globe. By distinguishing between violence-driven, theological, and sectarian content, we trace the connection between online rhetoric and key events on the ground. To the best of our knowledge, ours is one of the first studies to focus on Arabic content, while most literature focuses on English content. Our findings yield new important insights about how social media is used by radical militant groups to target the Arab-speaking world, and reveal important patterns in their propaganda efforts.

Since its early mainstream adoption in the 1990s, the Internet has been leveraged by white supremacist groups to recruit and radicalize individuals. Twenty years later, social media platforms, like YouTube, reddit, and Twitter, continue to further this practice. The attention of researchers has been primarily centered on white supremacist men, and this focus on white men erases white women’s roles as active agents in the spread of white supremacy, skewing our understanding of white supremacy as a whole. This study used digital ethnography and interviews to examine the ways white women are radicalized to organized white supremacy through popular social media platforms YouTube, reddit, and Twitter. The study found white women were radicalized by engaging with posts and joining communities focusing on beauty, anti-feminism or “The Red Pill,” traditionalist gender values or #TradLives, and alt-right politics. White supremacist recruiters leveraged gendered topics and weaponized platform features – likes, sharing, comments, recommendation algorithms, etc. – to cultivate a sense of community. Through involvement with these communities, women were introduced to racialized perspectives on each topic, usually after a catalytic pop culture or newsworthy event, and slowly radicalized to organized white supremacy.