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This article represents an initial exploration of the content and posting strategies of the current wave of racist flyer drops in the US, focusing specifically on a dataset of all documented flyers posted in 2018. The dataset was generated by the Anti-Defamation League (ADL) and augmented by the authors. The dataset is unique among open sources and includes a large number of incidents which were not reported in the media. The article consists of three parts. The first documents and briefly discusses the groups engaged in racist flyer development and drops. The second describes and characterises the text and image content of the flyers. The final section uses open sources and leaked material to describe the process by which flyer drops are instigated, planned and documented.

Policymakers have recently expressed concerns over the role of recommendation algorithms and their role in forming “filter bubbles”. This is a particularly prescient concern in the context of extremist content online; these algorithms may promote extremist content at the expense of more moderate voices. In this article, we make two contributions to this debate. Firstly, we provide a novel empirical analysis of three platforms’ recommendation systems when interacting with far-right content. We find that one platform—YouTube—does amplify extreme and fringe content, while two—Reddit and Gab—do not. Secondly, we contextualise these findings into the regulatory debate. There are currently few policy instruments for dealing with algorithmic amplification, and those that do exist largely focus on transparency. We argue that policymakers have yet to fully understand the problems inherent in “de-amplifying” legal, borderline content and argue that a co-regulatory approach may offer a route towards tackling many of these challenges.