Online Library

This case study explores the convergence of white extremist political ideology with mainstream political ideology on the micro-blogging platform Twitter – a phenomenon termed “inter-ideological mingling”. Exploring the spread of white extremism in the digital environment can provide insight into the growth of hate groups in the physical environment. A sample of 4800 tweets was examined through hierarchical cluster analysis and textual analysis. Several pieces of evidence were found supporting inter-ideological mingling. Cluster analysis shows that extremist terms are not isolated from terms found in mainstream political discourse. Textual analysis of individual tweets provides evidence for five strategies of inter-ideological mingling: joining, blending, piggybacking, backstaging, and narrating.

VOX-Pol Blog post.

Social media is a modern person’s digital voice to project and engage with new ideas and mobilise communities—a power shared with extremists. Given the societal risks of unvetted content-moderating algorithms for Extremism, Radicalisation, and Hate speech (ERH) detection, responsible software engineering must understand the who, what, when, where, and why such models are necessary to protect user safety and free expression. Hence, we propose and examine the unique research field of ERH context mining to unify disjoint studies. Specifically, we evaluate the start-to-finish design process from socio-technical definition-building and dataset collection strategies to technical algorithm design and performance. Our 2015–2021 51-study Systematic Literature Review (SLR) provides the first cross-examination of textual, network, and visual approaches to detecting extremist affiliation, hateful content, and radicalisation towards groups and movements. We identify consensus-driven ERH definitions and propose solutions to existing ideological and geographic biases, particularly due to the lack of research in Oceania/Australasia. Our hybridised investigation on Natural Language Processing, Community Detection, and visual-text models demonstrates the dominating performance of textual transformer-based algorithms. We conclude with vital recommendations for ERH context mining researchers and propose an uptake roadmap with guidelines for researchers, industries, and governments to enable a safer cyberspace.

As recent perpetrators of racially-motivated mass-shootings were visitors and contributors to online white supremacist networks, this article presents a preliminary model that attempts to understand the social psychological effects of interacting in these networks. Combining Jonathan Turner’s theory of the social psychological roots of extreme violence, scholarship on the white supremacist movement, and research on computer-mediated communication, it suggests that interactions in online white supremacist networks accelerate the conversion from secondary to tertiary deviance, amplify the negative emotional energy released by this conversion, and, under certain conditions, transform this energy into a violent kinetic force. The model is not unique to online white supremacist networks and will hopefully guide research in other types of networks as well.

From the new Facebook ‘Oversight Body’ for content moderation to the ‘Christchurch Call to eliminate terrorism and violent extremism online,’ a growing number of voluntary and non-binding informal governance initiatives have recently been proposed as attractive ways to rein in Facebook, Google, and other platform companies hosting user-generated content. Drawing on the literature on transnational corporate governance, this article reviews a number of informal arrangements governing online content on platforms in Europe, mapping them onto Abbott and Snidal’s (2009) ‘governance triangle’ model. I discuss three key dynamics shaping the success of informal governance arrangements: actor competencies, ‘legitimation politics,’ and inter-actor relationships of power and coercion.

In this paper we present a novel interactive multimodal learning system, which facilitates search and exploration in large networks of social multimedia users. It allows the analyst to identify and select users of interest, and to find similar users in an interactive learning setting. Our approach is based on novel multimodal representations of users, words and concepts, which we simultaneously learn by deploying a general-purpose neural embedding model. The usefulness of the approach is evaluated using artificial actors, which simulate user behavior in a relevance feedback scenario. Multiple experiments were conducted in order to evaluate the quality of our multimodal representations and compare different embedding strategies. We demonstrate the capabilities of the proposed approach on a multimedia collection originating from the violent online extremism forum Stormfront, which is particularly interesting due to the high semantic level of the discussions it features.