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