1. A computer-implemented method comprising: for each user of a group of users of a social networking system: generating an interest vector from a page affinity vector of the user, wherein: the page affinity vector of the user indicates, for each page of a plurality of pages of the social networking system, whether the user has expressly specified an affinity for the page, the interest vector indicates, for each concept of a plurality of concepts, whether the user is likely to have an interest in the concept, and the interest vector has fewer elements than the page affinity vector; clustering the group of users into a plurality of sub-groups by applying a distance function to the interest vectors of the users; for a first sub-group of the plurality of subgroups: identifying a centroid vector of the first sub-group based on the interest vectors of the users in the first sub-group; identifying user characteristics corresponding to the centroid vector; ranking each page of a plurality of pages on the social networking system with respect to the first sub-group based on the identified user characteristics, each page having an associated topic phrase; identifying a plurality of the highest-ranking pages; forming a textual description of the first sub-group comprising the topic phrases associated with the identified plurality of highest-ranking pages.