Diversity of Online Community Activities

TL;DR

This paper investigates the long-tailed distributions in online community activities, proposing local-behavior-based models that explain user engagement patterns and enable early content quality estimation.

Contribution

It introduces mechanisms based on local user behaviors to explain activity distributions and predict content quality, advancing understanding of online community dynamics.

Findings

Long-tails result from differences in user activity and content quality.

Models accurately explain overall behavior and predict early content quality.

Variability in user engagement significantly influences community activity patterns.

Abstract

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.