Online Library

Involuntary celibates, or incels, have been of heightened interest to scholars and practitioners due to their ongoing engagement in misogynistic and violent discourse. The incel subculture is complex, requiring unique strategies to develop effective interventions. The present study investigates patterns in incels’ online posting behaviors and whether acceptance of subcultural beliefs is reflected in variations of user engagement and posting behaviors over time. A sample of postings are drawn from a well-known incel-moderated forum and analyzed using group-based trajectory modeling. The results demonstrate that three distinct posting trajectory groups are present. The findings demonstrate heterogeneity among users’ posting behaviors in the forum over time and suggest that variation may be a reflection of users’ subcultural beliefs.

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.

VOX-Pol Blog Post.

VOX-Pol Blog post.

Since the inception of the World Wide Web, security agencies, researchers, and analysts have focused much of their attention on the sentiment found on hate-inspired web-forums. Here, one of their goals has been to detect and measure users’ affects that are expressed in the forums as well as identify how users’ affects change over time. Manual inspection has been one way to do this; however, as the number of discussion posts and sub-forums increase, there has been a growing need for an automated system that can assist humans in their analysis. The aim of this paper, then, is to detect and measure a number of affects expressed in written text on Stormfront.org, the most visited hate forum on the Web. To do this, we used a machine learning approach where we trained a model to recognize affects on three sub-forums: Ideology and Philosophy, For Stormfront Ladies Only, and Stormfront Ireland. The training data consisted of manual annotated data and the affects we focused on were racism, aggression, and worries. Results indicate that even though measuring affects is a subjective process, machine learning is a promising way forward to analyze and measure the presence of different affects on hate forums.