Online Library

Researchers have previously explored how right-wing extremists build a collective identity online by targeting their perceived “threat,” but little is known about how this “us” versus “them” dynamic evolves over time. This study uses a sentiment analysis-based algorithm that adapts criminal career measures, as well as semi-parametric group-based modeling, to evaluate how users’ anti-Semitic, anti-Black, and anti-LGBTQ posting behaviors develop on a sub-forum of the most conspicuous white supremacy forum. The results highlight the extent to which authors target their key adversaries over time, as well as the applicability of a criminal career approach in measuring radical posting trajectories online.

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.

VOX-Pol Blog post.

VOX-Pol Blog post.

VOX-Pol Blog post.

In recent years, researchers have shown a vested interest in developing advanced information technologies, machine-learning algorithms, and risk-assessment tools to detect and analyze radical content online, with increased attention on identifying violent extremists or measuring digital pathways of violent radicalization. Yet overlooked in this evolving space has been a systematic examination of what constitutes radical posting behaviors in general. This study uses a sentiment analysis-based algorithm that adapts criminal career measures – and is guided by communication research on social influence – to develop and describe three radical posting behaviors (high-intensity, high-frequency, and high-duration) found on a sub-forum of the most conspicuous right-wing extremist forum. The results highlight the multi-dimensional nature of radical right-wing posting behaviors, many of which may inform future risk factor frameworks used by law enforcement and intelligence agencies to identify credible threats online.