Online Library

Using a unique dataset of 227 convicted UK-based terrorists, this report fills a large gap in the existing literature. Using descriptive statistics, we first outline the degree to which various online activities related to radicalisation were present within the sample. The results illustrate the variance in behaviours often attributed to ‘online radicalisation’. Second, we conducted a smallest-space analysis to illustrate two clusters of commonly co-occurring behaviours that delineate behaviours from those directly associated with attack planning. Third, we conduct a series of bivariate and multivariate analyses to question whether those who interact virtually with like-minded individuals or learn online, exhibit markedly different experiences (e.g. radicalisation, event preparation, attack outcomes) than those who do not.

VOX-Pol Blog post.

This article describes and discusses a comparative semiotic analysis of online text collected from eight extreme right websites and four violent jihadi groups’ online magazines. The two datasets, which comprise just over 1 million words each, were analyzed using LIWC software. The core issues explored were the shared and different linguistic patterns used among extreme right and violent jihadi extremists and the emotional, cognitive, psychological, and social dimensions of the online textual discourses of each ideological grouping and what function these played in their overall political rhetoric. The findings bring to light some nuanced differences and similarities in the cognitive, social, psychological, and temporal dimensions of language used by each. For example, while both types of ideological text showed the same level of certainty in arguments as a cognitive process, the language depicting social and emotional processes, and religion were used more often by the violent jihadi extremists (VJEs) than the extreme right. The findings also point to the fact that VJEs were more likely than right-wing extremists to discuss the future and promise change as motivational incentives.

In addition to hosting user-generated video content, YouTube provides recommendation services, where sets of related and recommended videos are presented to users, based on factors such as co-visitation count and prior viewing history. This article is specifically concerned with extreme right (ER) video content, portions of which contravene hate laws and are thus illegal in certain countries, which are recommended by YouTube to some users. We develop a categorization of this content based on various schema found in a selection of academic literature on the ER, which is then used to demonstrate the political articulations of YouTube’s recommender system, particularly the narrowing of the range of content to which users are exposed and the potential impacts of this. For this purpose, we use two data sets of English and German language ER YouTube channels, along with channels suggested by YouTube’s related video service. A process is observable whereby users accessing an ER YouTube video are likely to be recommended further ER content, leading to immersion in an ideological bubble in just a few short clicks. The evidence presented in this article supports a shift of the almost exclusive focus on users as content creators and protagonists in extremist cyberspaces to also consider online platform providers as important actors in these same spaces.

Recent years have seen increased interest in the online presence of extreme right groups. Although originally composed of dedicated websites, the online extreme right milieu now spans multiple networks, including popular social media platforms such as Twitter, Facebook and YouTube. Ideally therefore, any contemporary analysis of online extreme right activity requires the consideration of multiple data sources, rather than being restricted to a single platform. We investigate the potential for Twitter to act as one possible gateway to communities within the wider online network of the extreme right, given its facility for the dissemination of content. A strategy for representing heterogeneous network data with a single homogeneous network for the purpose of community detection is presented, where these inherently dynamic communities are tracked over time. We use this strategy to discover and analyse persistent English and German language extreme right communities.

Many extreme right groups have had an online presence for some time through the use of dedicated websites. This has been accompanied by increased activity in social media websites in recent years, which may enable the dissemination of extreme right content to a wider audience. In this paper, we present exploratory analysis of the activity of a selection of such groups on Twitter, using network representations based on reciprocal follower and mentions interactions. We find that stable communities of related users are present within individual country networks, where these communities are usually associated with variants of extreme right ideology. Furthermore, we also identify the presence of international relationships between certain groups across geopolitical boundaries.