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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.

VOX-Pol Blog Post.

VOX-Pol Blog Post.

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.

Objectives
Despite popular notions of “filter bubbles” and “echo chambers” contributing to radicalization, little evidence exists to support these hypotheses. However, social structure social learning theory would suggest a hereto untested interaction effect.
Methodology
An RCT of new Twitter users in which participants were randomly assigned to a treatment of “filter bubble” (personalization algorithm) suppression. Ego-centric network and survey data were combined to test the effects on justification for suicide bombings.
Findings
Statistically significant interaction effects were found for two proxies of the echo chamber, the E-I index and modularity. For the treatment group, higher scores on both factors decreased the likelihood for radicalization, with opposing trends in the control group.
Conclusions
The echo chamber effect may be dependent on the filter bubble. More research is needed on online network structures in radicalization. While personalization algorithms can potentially be harmful, they may also be leveraged to facilitate interventions.

Over the last decade, there has been an increased focus among researchers on the role of the Internet among actors and groups across the political and ideological spectrum. There has been particular emphasis on the ways that far-right extremists utilize forums and social media to express ideological beliefs through sites affiliated with real-world extremist groups and unaffiliated websites. The majority of research has used qualitative assessments or quantitative analyses of keywords to assess the extent of specific messages. Few have considered the breadth of extremist ideologies expressed among participants so as to quantify the proportion of beliefs espoused by participants. This study addressed this gap in the literature through a content analysis of over 18,000 posts from eight far-right extremist forums operating online. The findings demonstrated that the most prevalent ideological sentiments expressed in users’ posts involved anti-minority comments, though they represent a small proportion of all posts made in the sample. Additionally, users expressed associations to far-right extremist ideologies through their usernames, signatures, and images associated with their accounts. The implications of this analysis for policy and practice to disrupt extremist movements were discussed in detail.