The explosive growth in image sharing via social networks has produced exciting opportunities for the computer vision community in areas including face, text, product and scene recognition. In this work we turn our attention to group photos of people and ask the question: what can we determine about the social subculture or urban tribe to which these people belong? To this end, we propose a framework employing low- and mid-level features to capture the visual attributes distinctive to a variety of urban tribes. We proceed in a semi-supervised manner, employing a metric that allows us to extrapolate from a small number of pairwise image similarities to induce a set of groups that visually correspond to familiar urban tribes such as biker, hipster or goth. Automatic recognition of such information in group photos offers the potential to improve recommendation services, context sensitive advertising and other social analysis applications. We present promising preliminary experimental results that demonstrate our ability to categorize group photos in a socially meaningful manner.