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The online presence of incels, or involuntary celibates, has been an increasing security concern for researchers, practitioners, and policymakers in recent years, given that self-identified incels – including Alek Minassian and Elliot Rodger – used the Internet to disseminate incel ideology and manifestos prior to committing acts of violence. However, little is empirically known about the incel movement in general or their online communities in particular. The present study draws from a set of comments from r/Incels, a now defunct but once popular subreddit dedicated to the incel community, and compares the most highly-upvoted comments (n = 500) to a random set of other comments (n = 500) in the subreddit. This qualitative analysis focuses on identifying subcultural discourse that is widely supported and engaged with by members of the online community and the extent to which incels utilize this online space to reaffirm deviant behavior. Our study underscores the importance, as well as the difficulties, of drawing from online sources like web-forums to generate new knowledge on deviant communities and behaviors. We conclude with a discussion of the implications of this analysis, its limitations, and avenues for future research.

The Islamic State in Iraq and Syria (ISIS), like no other terrorist organization before, has used Twitter and other social media channels to broadcast its message, inspire followers, and recruit new fighters. Though much less heralded, ISIS opponents have also taken to Twitter to castigate the ISIS message. This report draws on publicly available Twitter data to examine this ongoing debate about ISIS on Arabic Twitter and to better understand the networks of ISIS supporters and opponents on Twitter. To support the countermessaging effort and to more deeply understand ISIS supporters and opponents, this study uses a mixed-methods analytic approach to identify and characterize in detail both ISIS support and opposition networks on Twitter. This analytic approach draws on community detection algorithms that help detect interactive communities of Twitter users, lexical analysis that can identify key themes and content for large data sets, and social network analysis.

Despite the ongoing need for practitioners to identify violent extremists online before their engagement in violence offline, little is empirically known about their digital footprints generally or differences in their posting behaviors compared to their non-violent counterparts particularly – especially on high-frequency posting days. Content analysis was used to examine postings from a unique sample of violent and non-violent right-wing extremists as well as from a sample of postings within a sub-forum of the largest white supremacy forum during peak and non-peak posting days for comparison purposes. Several noteworthy posting behaviors were identified that may assist in identifying credible threats online.

VOX-Pol Blog post.

VOX-Pol Blog post.

Although there is an ongoing need for law enforcement and intelligence agencies to identify and assess the online activities of violent extremists prior to their engagement in violence offline, little is empirically known about their online posting patterns generally or differences in their online patterns compared to non-violent extremists who share similar ideological beliefs particularly. Even less is empirically known about how their online patterns compare to those who post in extremist spaces in general. This study addresses this gap through a content analysis of postings from a unique sample of violent and non-violent right-wing extremists as well as from a sample of postings within a sub-forum of the largest white supremacy web-forum, Stormfront. Here the existence of extremist ideologies, personal grievances, and violent extremist mobilization efforts were quantified within each of the three sample groups. Several notable differences in posting patterns were observed across samples, many of which may inform future risk factor frameworks used by law enforcement and intelligence agencies to identify credible threats online. This study concludes with a discussion of the implications of the analysis, its limitations, and avenues for future research.