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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.

This study examined the interspecific amplification of nuclear microsatellite loci developed mainly for eucalypts in the subgenus Symphyomyrtus across five species within the second most speciose subgenus, subgenus Eucalyptus. A set of eight to 10 loci, depending on taxon, have been identified that are highly variable and easily scored. The successful transfer of microsatellite loci to these eucalypt species sidesteps the expensive and time-consuming development of species-specific microsatellite libraries. This primer set will enable the examination and cross-species comparison of the genetic resources of commercially and ecologically important members of the subgenus Eucalyptus.