Empirical study and modeling of human behaviour dynamics of comments on Blog posts

TL;DR

This paper empirically analyzes comment timing on blogs, finds a power-law distribution, and proposes a model with decaying interest that explains the observed dynamics.

Contribution

It introduces a new model with decaying interest to explain comment timing, supported by empirical data and rigorous analysis.

Findings

Comment intervals follow a power-law distribution.

The proposed model accurately reproduces empirical interval distributions.

Model parameters can tune the power-law exponent.

Abstract

On-line communities offer a great opportunity to investigate human dynamics, because much information about individuals is registered in databases. In this paper, based on data statistics of online comments on Blog posts, we first present an empirical study of a comment arrival-time interval distribution. We find that people interested in some subjects gradually disappear and the interval distribution is a power law. According to this feature, we propose a model with gradually decaying interest. We give a rigorous analysis on the model by non-homogeneous Poisson processes and obtain an analytic expression of the interval distribution. Our analysis indicates that the time interval between two consecutive events follows the power-law distribution with a tunable exponent, which can be controlled by the model parameters and is in interval (1,+{\infty}). The analytical result agrees with the empirical results well, obeying an approximately power-law form. Our model provides a theoretical basis for human behaviour dynamics of comments on Blog posts.