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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.

Terror attacks have been linked in part to online extremist content. Although tens of thousands of Islamist extremism supporters consume such content, they are a small fraction relative to peaceful Muslims. The efforts to contain the ever-evolving extremism on social media platforms have remained inadequate and mostly ineffective. Divergent extremist and mainstream contexts challenge machine interpretation, with a particular threat to the precision of classification algorithms. Our context-aware computational approach to the analysis of extremist content on Twitter breaks down this persuasion process into building blocks that acknowledge inherent ambiguity and sparsity that likely challenge both manual and automated classification. We model this process using a combination of three contextual dimensions — religion, ideology, and hate — each elucidating a degree of radicalization and highlighting independent features to render them computationally accessible. We utilize domain-specific knowledge resources for each of these contextual dimensions such as Qur’an for religion, the books of extremist ideologues and preachers for political ideology and a social media hate speech corpus for hate. Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate that reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming. Given the potentially significant social impact, we evaluate the performance of our algorithms to minimize mislabeling, where our approach outperforms a competitive baseline by 10.2% in precision.

By reviewing studies of how today’s terrorist and extremist groups operate on social media in conjunction with an overview of U.S. government regulation of terrorist content online, this report finds that stricter U.S. regulation of social media providers may not be the most effective method of combating online terrorist and extremist content.

Security informatics and intelligence computation plays a vital role in detecting and classifying terrorism contents in the web. Accurate web content classification using the computational intelligence and security informatics will increase the opportunities of the early detection of the potential terrorist activities. In this paper, we propose a modified frequency-based term weighting scheme for accurate Dark Web content classification. The proposed term weighting scheme is compared to the common techniques used in text classification such as Term Frequency (TF), Term Frequency-Inverse Document Frequency (TF-IFD), and Term Frequency- Relative Frequency (tf.rf), on a dataset selected from Dark Web Portal Forum. The experimental results show that the classification accuracy and other evaluation measures based on the proposed scheme outperforms other term weighting techniques based classification.

VOX-Pol Blog post.

Reciprocal radicalisation is the theory that extremist organisations are connected and feed on one another’s rhetoric and actions to justify violent escalation. Recent empirical work has suggested that reciprocal radicalisation is a good deal more subtle than is often assumed, and is nuanced by organisational, social and political context. This study seeks to apply the theory of reciprocal radicalisation to the far-right digital milieu, an online space conceptualised as underpinning the varying physical manifestations of the far-right. Based on a qualitative thematic analysis of user posts in three far-right web forums, the study concludes that responses to ideologically opposed terrorism within the far-right milieu are often at odds with the assumed radicalising effects of terrorist attacks. While responses were not uniform, for many users in the far-right digital milieu, jihadist terrorism was an obvious and expected result of the wider failures of politics and society. Although there were some calls for violent reprisal, they were juxtaposed by non-violent responses which interpreted jihadist terror as a consequence and sign of societal decadence and political weakness around issues of migration and rights.