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 article reports and discusses the results of a study that investigated photographic images of children in five online terrorist magazines to understand the roles of children in these groups. The analysis encompasses issues of Inspire, Dabiq, Jihad Recollections (JR), Azan, and Gaidi Mtanni (GM) from 2009 to 2016. The total number of images was ninety-four. A news value framework was applied that systematically investigated what values the images held that resulted in them being "newsworthy" enough to be published. This article discusses the key findings, which were that Dabiq distinguished different roles for boys and girls, portrayed fierce and prestigious boy child perpetrators, and children flourishing under the caliphate; Inspire and Azan focused on portraying children as victims of Western-backed warfare; GM portrayed children supporting the cause peacefully; and JR contained no re-occurring findings.

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