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Many researchers, practitioners, and policymakers are concerned about online communities that are known to facilitate violent right-wing extremism, but little is empirically known about these digital spaces in general and the developmental posting behaviors that make up these spaces in particular. In this study, group-based trajectory modeling—derived from a criminal career paradigm—was used to identify posting trajectories found in the open-access sections of the Iron March and Fascist Forge forums, both of which have gained notoriety for their members’ online advocacy of violence and acts of violence carried out by them. Multinomial logistic regression and analysis of variance were then used to assess whether posters’ time of entry into the violent forums predicted trajectory group assignment. Overall, the results highlight a number of similarities and differences in posting behaviors within and across platforms, many of which may inform future risk factor frameworks used by law enforcement and intelligence agencies to identify credible threats online. We conclude with a discussion of the implications of this analysis, followed by a discussion of study limitations and avenues for future research.

While a growing body of evidence suggests that the Internet is a key facilitator of violent extremism, research in this area has rarely incorporated former extremists’ experiences with the Internet when they were involved in violent extremism. To address this gap, in-depth interviews were conducted with ten Canadian former right-wing extremists who were involved in violent racist skinhead groups, with interview questions provided by thirty Canadian law enforcement officials and ten community activists. Participants were asked about their use of the Internet and the connection between their on- and offline worlds during their involvement in the violent right-wing extremist movement. Overall, our study findings highlight the interplay between the Internet and violent extremism as well as the interactions between the on- and offline worlds of violent extremists. We conclude with a discussion of study limitations and avenues for future research.

To see the Internet as only a ‘tool’ or ‘resource’ for disseminating ideas and products, as much of the literature has done, is to miss an even more significant aspect of online venues. The Internet is also a site of important ‘identity work’, in which collective identities can be accomplished interactively. This chapter explores how collective identities are constructed by white supremacists who specifically exploit the web as a venue for expressing ‘white pride worldwide’. Drawing on social movement literature around the building of collective identities, we examine the online identity work of the ‘globalizing’ right-wing extremist movement through four key frames: alternative media/alternative messaging; identity borders; shared identity; and mobilizing hate. Here, we explore the Internet not as a tool, but as site for the active construction of collective white identity.

Since the advent of the Internet, right-wing extremists and those who subscribe to extreme right views have exploited online platforms to build a collective identity among the like-minded. Research in this area has largely focused on extremists’ use of websites, forums, and mainstream social media sites, but overlooked in this research has been an exploration of the popular social news aggregation site Reddit. The current study explores the role of Reddit’s unique voting algorithm in facilitating “othering” discourse and, by extension, collective identity formation among members of a notoriously hateful subreddit community, r/The_Donald. The results of the thematic analysis indicate that those who post extreme-right content on r/The_Donald use Reddit’s voting algorithm as a tool to mobilize like-minded members by promoting extreme discourses against two prominent out-groups: Muslims and the Left. Overall, r/The_Donald’s “sense of community” facilitates identity work among its members by creating an environment wherein extreme right views are continuously validated.

Although many law enforcement and intelligence agencies are concerned about online communities known to facilitate violent right-wing extremism, little is empirically known about the presence of extremist ideologies, expressed grievances, or violent mobilization efforts that make up these spaces. In this study, we conducted a content analysis of a sample of postings from two of the most conspicuous right-wing extremist forums known for facilitating violent extremism, Iron March and Fascist Forge. We identified a number of noteworthy posting patterns within and across forums that may assist law enforcement and intelligence agencies in identifying credible threats online.

Despite the ongoing need for researchers, practitioners, and policymakers to identify and assess the online activities of violent extremists prior to their engagement in violence offline, little is empirically known about their online behaviors generally or differences in their posting behaviors compared to non-violent extremists who share similar ideological beliefs particularly. In this study, we drew from a unique sample of violent and non-violent right-wing extremists to compare their posting behaviors within a sub-forum of the largest white supremacy web-forum. Analyses for the current study proceeded in three phases. First, we plotted the average posting trajectory for users in the sample, followed by an assessment of the rates at which they stayed active or went dormant in the sub-forum. We then used logistic regression to examine whether specific posting behaviors were characteristic of users’ violence status. The results highlight a number of noteworthy differences in the posting behaviors of violent and non-violent right-wing extremists, many of which may inform future risk factor frameworks used by law enforcement and intelligence agencies to identify credible threats online. We conclude with a discussion of the implications of this analysis, its limitations and avenues for future research.