Homogeneous temporal activity patterns in a large online communication space

TL;DR

This study reveals regular, predictable temporal activity patterns in online social communication, characterized by log-normal distributions and oscillatory cycles, despite community heterogeneity.

Contribution

It uncovers homogeneous temporal activity patterns and models them with simple statistical distributions, advancing understanding of online community dynamics.

Findings

Reaction times follow log-normal distributions.

Daily and weekly oscillations influence activity patterns.

Few parameters suffice to describe and predict community behavior.

Abstract

The many-to-many social communication activity on the popular technology-news website Slashdot has been studied. We have concentrated on the dynamics of message production without considering semantic relations and have found regular temporal patterns in the reaction time of the community to a news-post as well as in single user behavior. The statistics of these activities follow log-normal distributions. Daily and weekly oscillatory cycles, which cause slight variations of this simple behavior, are identified. A superposition of two log-normal distributions can account for these variations. The findings are remarkable since the distribution of the number of comments per users, which is also analyzed, indicates a great amount of heterogeneity in the community. The reader may find surprising that only a few parameters allow a detailed description, or even prediction, of social many-to-many information exchange in this kind of popular public spaces.