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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.

As efforts to identify and remove online terrorist and violent extremist content have intensified, concern has also grown about so‐called lawful but awful content. Various options have been touted for reducing the visibility of this borderline content, including removing it from search and recommendation algorithms, downranking it and redirecting those who search for it. This article contributes to this discussion by considering the moderation of such content, in terms of three sets of values. First, definitional clarity. This is necessary to provide users with fair warning of what content is liable to moderation and to place limits on the discretion of content moderators. Yet, at present, definitions of borderline content are vague and imprecise. Second, necessity and proportionality. While downranking and removal from search and recommender algorithms should be distinguished from deplatforming, tech companies’ efforts to deamplify borderline content give rise to many of the same concerns as content removal and account shutdowns. Third, transparency. While a number of platforms now publish their content moderation policies and transparency data reports, these largely focus on violative, not borderline content. Moreover, there remain questions around access to data for independent researchers and transparency at the level of the individual user.

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.

VOX-Pol Blog Post.

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.