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In response to the rise of various fringe movements in recent years, from anti-vaxxers to QAnon, there has been increased public and scholarly attention to misinformation and conspiracy theories and the online communities that produce them. However, efforts at understanding the radicalisation process largely focus on those who go on to commit violent crimes. This article draws on three waves of research exploring the experiences of individuals currently or formerly involved in fringe communities, including the different stages of investment they progressed through, and ultimately, what made people leave. We propose a pathway model for understanding contemporary online radicalisation, including potential interventions that could be safely made at each stage. Insight into the experience of being immersed in these communities is essential for engaging with these people empathetically, and therefore preventing both the emergence of violent terrorists and protecting vulnerable people from being drawn into these communities.

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.

VOX-Pol Blog post.

Many cases of radicalism, especially violent radicalism, manifest within the context of inter-ethnic conflict and war. Research in this domain has significantly contributed to our overarching comprehension of the issue. This form of radicalism is inherently linked to the dynamics of group affiliation. However, our focus here is directed towards the individual motivation driving radical actions. While radical actions may encompass dynamics of group affiliation (e.g., the most radical pro-life activists being members of the Army of God), they also possess a substantial individual component. For instance, numerous attacks on abortion doctors or clinics are categorized as ’lone-wolf terrorism’.

In this study, we sought to examine whether state-of-the-art Machine Learning tech- nology for text analysis could effectively discern the intent behind radical actions from user-generated content. We analyzed a diverse array of radical texts published online by various activists, including anarchists, Iranian guerrilla members, Indian revolution- aries, activists from the US civil rights movement, and The Army of God. Our analysis reveals that Machine Learning models can successfully identify potential signals indica- tive of the author’s intent behind radical actions.

The report begins by outlining some of the broad knowledge around the idea of mass persuasion, before focusing specifically on lessons that have been learned in the field of P/CVE. This is followed by a synthesis of existing “How To” guides for the creation of strategic communications from a range of policy and practitioner stakeholders. Then, we discuss specific knowledge of audiovisual content, particularly considerations for short-form video content. The report concludes by outlining how stakeholders, including social media platforms, can monitor, measure, and evaluate the impact of this type of content.

Exposure to radicalizing information has been associated with support for violent extremism. It is, however, unclear whether specific information use behavior, namely, a distinct pattern of incidental exposure (IE) to and active selection (AS) of radicalizing content, indicates stronger violent extremist attitudes and radical action intentions. Drawing on a representative general population sample (N = 1509) and applying latent class analysis, we addressed this gap in the literature. Results highlighted six types of information use behavior. The largest group of participants reported a near to zero probability of both IE to and AS of radicalizing material. Two groups of participants were characterized by high or moderate probabilities of incidental exposure as well as a low probability of active selection of radicalizing content. The remaining groups displayed either low, moderate, or high probabilities of both IE and AS. Importantly, we showed between-group differences regarding violent extremist attitudes and radical behavioral intentions. Individuals reporting near zero or high probabilities for both IE to and AS of radicalizing information expressed the lowest and strongest violent extremist attitudes and willingness to use violence respectively. Groups defined by even moderate probabilities of AS endorsed violent extremism more strongly than those for which the probability for incidental exposure was moderate or high but AS of radicalizing content was unlikely.