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As violent extremists continue to surface in online discussion forums, law enforcement agencies search for new ways of uncovering their digital indicators. Researchers have both described and hypothesized a number of ways to detect online traces of potential extremists, yet this area of inquiry remains in its infancy. This study proposes a new search method that, through the analysis of sentiment, identifies the most radical users within online forums. Although this method is applicable to web-forums of any type, the method was evaluated on four Islamic forums containing approximately 1 million posts of its 26,000 unique users. Several characteristics of each user’s postings were examined, including their posting behavior and the content of their posts. The content was analyzed using Parts-Of-Speech tagging, sentiment analysis, and a novel algorithm called ‘Sentiment-based Identification of Radical Authors’, which accounts for a user’s percentile score for average sentiment score, volume of negative posts, severity of negative posts, and duration of negative posts. The results suggest that there is no simple typology that best describes radical users online; however, the method is flexible enough to evaluate several properties of a user’s online activity that can identify radical users on the forums.

VOX-Pol Blog post.

This policy brief traces how Western right-wing extremists have exploited the power of the internet from early dial-up bulletin board systems to contemporary social media and messaging apps. It demonstrates how the extreme right has been quick to adopt a variety of emerging online tools, not only to connect with the like-minded, but to radicalise some audiences while intimidating others, and ultimately to recruit new members, some of whom have engaged in hate crimes and/or terrorism. Highlighted throughout is the fast pace of change of both the internet and its associated platforms and technologies, on the one hand, and the extreme right, on the other, as well as how these have interacted and evolved over time. Underlined too is the persistence, despite these changes, of rightwing extremists’ online presence, which poses challenges for effectively responding to this activity moving forward.

Right-wing extremist groups harness popular social media platforms to accrue and mobilize followers. In recent years, researchers have examined the various themes and narratives espoused by extremist groups in the United States and Europe, and how these themes and narratives are employed to mobilize their followings on social media. Little, however, is comparatively known about how such efforts unfold within and between right-wing extremist groups in Australia and Canada. In this study, we conducted a cross-national comparative analysis of over eight years of online content found on 59 Australian and Canadian right-wing group pages on Facebook. Here we assessed the level of active and passive user engagement with posts and identified certain themes and narratives that generated the most user engagement. Overall, a number of ideological and behavioral commonalities and differences emerged in regard to patterns of active and passive user engagement, and the character of three prevailing themes: methods of violence, and references to national and racial identities. The results highlight the influence of both the national and transnational context in negotiating which themes and narratives resonate with Australian and Canadian right-wing online communities, and the multi-dimensional nature of right-wing user engagement and social mobilization on social media.

Researchers have previously explored how right-wing extremists build a collective identity online by targeting their perceived “threat,” but little is known about how this “us” versus “them” dynamic evolves over time. This study uses a sentiment analysis-based algorithm that adapts criminal career measures, as well as semi-parametric group-based modeling, to evaluate how users’ anti-Semitic, anti-Black, and anti-LGBTQ posting behaviors develop on a sub-forum of the most conspicuous white supremacy forum. The results highlight the extent to which authors target their key adversaries over time, as well as the applicability of a criminal career approach in measuring radical posting trajectories online.

Since the inception of the World Wide Web, security agencies, researchers, and analysts have focused much of their attention on the sentiment found on hate-inspired web-forums. Here, one of their goals has been to detect and measure users’ affects that are expressed in the forums as well as identify how users’ affects change over time. Manual inspection has been one way to do this; however, as the number of discussion posts and sub-forums increase, there has been a growing need for an automated system that can assist humans in their analysis. The aim of this paper, then, is to detect and measure a number of affects expressed in written text on Stormfront.org, the most visited hate forum on the Web. To do this, we used a machine learning approach where we trained a model to recognize affects on three sub-forums: Ideology and Philosophy, For Stormfront Ladies Only, and Stormfront Ireland. The training data consisted of manual annotated data and the affects we focused on were racism, aggression, and worries. Results indicate that even though measuring affects is a subjective process, machine learning is a promising way forward to analyze and measure the presence of different affects on hate forums.