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The RAN small-scale expert meeting on (young) women’s usage of social media and lessons learned for preventing violet extremism (PVE) was aimed at unpacking some of the gaps. This paper summarises the highlights of the discussion, discusses the vulnerabilities that are specific to (young) women, explains how recruiters use these vulnerabilities online and, finally, presents the recommendations that the experts stressed during the meeting.

Previously theorised as vehicles for expressing progressive dissent, this article considers how political memes have become entangled in the recent reactionary turn of web subcultures. Drawing on Chantal Mouffe’s work on political affect, this article examines how online anonymous communities use memetic literacy, memetic abstraction, and memetic antagonism to constitute themselves as political collectives. Specifically, it focuses on how the subcultural and highly reactionary milieu of 4chan’s /pol/ board does so through an anti-Semitic meme called triple parentheses. In aggregating the contents of this peculiar meme from a large dataset of /pol/ comments, the article finds that /pol/ users, or anons, tend to use the meme to formulate a nebulous out-group resonant with populist demagoguery.

VOX-Pol Blog post.

The reception to 2020’s The Last of Us Part II was a maelstrom of misleading marketing, unprecedented leaks, and a vicious fallout characterized by prejudiced online harassment and sprawling conspiracies. Through an in-depth analysis of Part II‘s reception, this article seeks to apprehend the increasing frequency of such controversies in popular culture as a distinct transformation of online fandom, which is defined by the agendas of the alt-right. The “anti-woke” campaigns emblematic of these communities are best understood through what this article defines as alt-fandom, where conspiracy theories are fabricated in order to defy the supposed ideological and narrative transgressions of a new text. In the case of The Last of Us franchise, the challenges posed by its corrosive alt-fandom are endemic to a new reception climate confronting the production of media texts.

Academics are increasingly expected to use social media to disseminate their work and knowledge to public audiences. Although this has various advantages, particularly for alternative forms of dissemination, the web can also be an unsafe space for typically oppressed or subordinated groups. This article presents two auto-ethnographic accounts of the abuse and hate academics researching oppressed groups, namely, women and people of Muslim faith, experienced online. In doing so, this article falls into four parts. The first section provides an overview of existing literature, particularly focusing on work which explores the violence and abuse of women and people of Muslim faith online. The second section considers the auto-ethnographic methodological approach adopted in this article. The third section provides the auto-ethnographic accounts of the author’s experiences of hate and abuse online. The final section locates these experiences within broader theoretical concepts, such as silencing, and considers possible implications of such online hate in both an academic context and beyond.

Video sharing platforms like YouTube are increasingly targeted by aggression and hate attacks. Prior work has shown how these attacks often take place as a result of “raids,” i.e., organized efforts by ad-hoc mobs coordinating from third-party communities. Despite the increasing relevance of this phenomenon, however, online services often lack effective countermeasures to mitigate it. Unlike well-studied problems like spam and phishing, coordinated aggressive behavior both targets and is perpetrated by humans, making defense mechanisms that look for automated activity unsuitable. Therefore, the de-facto solution is to reactively rely on user reports and human moderation. In this paper, we propose an automated solution to identify YouTube videos that are likely to be targeted by coordinated harassers from fringe communities like 4chan. First, we characterize and model YouTube videos along several axes (metadata, audio transcripts, thumbnails) based on a ground truth dataset of videos that were targeted by raids. Then, we use an ensemble of classifiers to determine the likelihood that a video will be raided with very good results (AUC up to 94%). Overall, our work provides an important first step towards deploying proactive systems to detect and mitigate coordinated hate attacks on platforms like YouTube.