Individual popularity and activity in online social systems

TL;DR

This paper introduces a stochastic model to explain how individual popularity and activity patterns emerge in online social systems, capturing real-world heavy-tailed distributions through simple underlying mechanisms.

Contribution

It presents a novel stochastic model that links attraction kernels and growth patterns to the statistical properties of user and item popularity and activity.

Findings

Different growth and attraction patterns produce heavy-tailed distributions.

The model explains the complex dynamics with simple underlying principles.

It provides insights into the mechanisms behind popularity and activity in social systems.

Abstract

We propose a stochastic model of web user behaviors in online social systems, and study the influence of attraction kernel on statistical property of user or item occurrence. Combining the different growth patterns of new entities and attraction patterns of old ones, different heavy-tailed distributions for popularity and activity which have been observed in real life, can be obtained. From a broader perspective, we explore the underlying principle governing the statistical feature of individual popularity and activity in online social systems and point out the potential simple mechanism underlying the complex dynamics of the systems.