Web sites where users create and rate content display long-tailed distributions in many aspects of behavior. Using one such community site, Essembly, we propose and evaluate mechanisms to explain these behaviors. Unlike purely descriptive models, these mechanisms rely on user behaviors based on information available to each user. For Essembly, we find the long-tails arise from large differences among user activity rates, the time users devote to the site, and qualities of the rated content. The models not only explain overall behavior but also allow estimating the properties of users and content from their early behaviors.