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Major social media platforms have recently taken a more proactive stand against harmful far-right content and pandemic-related disinformation on their sites. However, these actions have catalysed the growth of fringe online social networks for participants seeking right-wing content, safe havens, and unhindered communication channels. To better understand these isolated systems of online activity and their success, the study on Gab Social examines the mechanisms used by the far right to form an alternative collective on fringe social media. My analysis showcases how these online communities are built by perpetuating meso-level identity-building narratives. By examining Gab’s emphasis on creating its lasting community base, the work offers an experiential examination of the different communication devices and multimedia within the platform through a netnographic and qualitative content analysis lens. The emergent findings and discussion detail the far right’s virtual community-building model, revolving around its sense of in-group superiority and the self-reinforcing mechanisms of collective. Not only does this have implications for understanding Gab’s communicative dynamics as an essential socialisation space and promoter of a unique meso-level character, but it also reflects the need for researchers to (re)emphasise identity, community, and collectives in far-right fringe spaces.

From politicians to podcast hosts, online platforms have systematically banned (“deplatformed”) influential users for breaking platform guidelines. Previous inquiries on the effectiveness of this intervention are inconclusive because 1) they consider only few deplatforming events; 2) they consider only overt engagement traces (e.g., likes and posts) but not passive engagement (e.g., views); 3) they do not consider all the potential places users impacted by the deplatforming event might migrate to. We address these limitations in a longitudinal, quasi-experimental study of 165 deplatforming events targeted at 101 influencers. We collect deplatforming events from Reddit posts and then manually curate the data, ensuring the correctness of a large dataset of deplatforming events. Then, we link these events to Google Trends and Wikipedia page views, platform-agnostic measures of online attention that capture the general public’s interest in specific influencers. Through a difference-in-differences approach, we find that deplatforming reduces online attention toward influencers. After 12 months, we estimate that online attention toward deplatformed influencers is reduced by -63% (95% CI [-75%,-46%]) on Google and by -43% (95% CI [-57%,-24%]) on Wikipedia. Further, as we study over a hundred deplatforming events, we can analyze in which cases deplatforming is more or less impactful, revealing nuances about the intervention. Notably, we find that both permanent and temporary deplatforming reduce online attention toward influencers; Overall, this work contributes to the ongoing effort to map the effectiveness of content moderation interventions, driving platform governance away from speculation.

Little is known about online behaviours of violent extremists generally or differences compared to non-violent extremists who share ideological beliefs. Even less is known about desistance from posting behaviour. A sample of 99 violent and non-violent right-wing extremists to compare their online patterns of desistance within a sub-forum of the largest white supremacy web-forum was analysed. A probabilistic model of desistance was tested to determine the validity of criteria set for users reaching posting desistance. Findings indicated that the criteria predicted “true” desistance, with 5% misidentification. Each consecutive month without posting in the sub-forum resulted in a 7.6% increase in odds of posting desistance. There were no significant differences in effects for violent versus non-violent users, though statistical power was low.

Violent extremist, terrorist, and targeted hate actors have been actively exploiting video games to propagandise, recruit and fundraise for more than 30 years. This report presents an analysis of that history using a unique dataset, the Extremist and Terrorist Games Database (ETGD), developed by the authors. It contains 155 reviewed entries of standalone games, modifications for existing games (mods) and browser‑based games dating from 1982 to 2024.

The titles analysed appear across the ideological spectrum: far right (101 titles), jihadist (24), far left (1) and other forms of extremism and targeted hate (29), including school‑massacre ideation (12). They span platforms ranging from simple standalone games for Atari in the 1980s to sophisticated mods for some of today’s most popular games. The number of titles has increased year on year – in line with global conflict and extremist ideological trends, and revealing a continued push by malicious actors to exploit gaming.

Far from being a comprehensive analysis of the ETGD, we intend this preliminary launch report to form a basis for future research of the dataset and a framework for continued contributions to the ETGD from Extremism and Gaming Research Network (EGRN) members. Above all, we seek to contribute to sensible policymaking to prevent violent extremism that situates games as part of a wider contested and exploited information space, which deserves far more attention from those working towards peaceful ends.