Heavy-tailed statistics in short-message communication

TL;DR

This paper analyzes short-message communication patterns, revealing heavy-tailed distributions in interevent times and message counts, indicating non-Poisson human activity behaviors in modern messaging systems.

Contribution

It provides empirical evidence of heavy-tailed statistics in short-message communication, highlighting non-Poisson activity patterns at the individual level.

Findings

Interevent times follow heavy-tailed distributions

Message counts per conversation are heavy-tailed

Human activity patterns are non-Poisson

Abstract

Short-message (SM) is one of the most frequently used communication channels in the modern society. In this Brief Report, based on the SM communication records provided by some volunteers, we investigate the statistics of SM communication pattern, including the interevent time distributions between two consecutive short messages and two conversations, and the distribution of message number contained by a complete conversation. In the individual level, the current empirical data raises a strong evidence that the human activity pattern, exhibiting a heavy-tailed interevent time distribution, is driven by a non-Poisson nature.