Tactical interventions in online hate speech : the case of #stopIslam

This article sets out findings from a project focused on #stopIslam, a hashtag that gained prominence following the Brussels terror attack of 2016. We initially outline a big data analysis which shows how counter-narratives – criticizing #stopIslam – momentarily subverted negative news reporting of Muslims. The rest of the article details qualitative findings that complicate this initial positive picture. We set out key tactics engaged in by right-wing actors, self-identified Muslim users, would-be allies and celebrities and elucidate how these tactics were instrumental in the direction, dynamics and legacies of the hashtag. We argue that the tactical interventions of tightly bound networks of right-wing actors, as well as the structural constraints of the platform, not only undermined the longevity and coherence of the counter-narratives but subtly modulated the affordances of Twitter in ways that enabled these users to extend their voice outwards, reinforcing long-standing representational inequalities in the process

Fear filter: Visualising the UK terror threat level

Fear Filter is a digital artwork comprising of a mobile phone photo filters application for Android / iOS platforms and a digital photo stream. The photo filters are created by gathering current and historical information about the UK Threat Level from a live feed from MI5, the UK security service. Photographs taken with the mobile application are transformed by the photo filters, each of which correspond to a different moment in time and the related Threat Level from that period. The filters cover the period 1 August 2006 until the present moment. Photos shared from the mobile application are automatically posted to a public photo stream.Fear Filter exploits the confluence of mobile digital photography, platforms, networks and the online security theatre of the UK Threat Level to reformulate the relationship between photography and terrorism