Lognormal Infection Times of Online Information Spread

TL;DR

This paper models the infection times in online information spread as a convolution of lognormal distributions, supported by experimental data, revealing insights into human behavior and contagion dynamics.

Contribution

It introduces a lognormal convolution model for infection times in online information spread, supported by experimental evidence, linking to human behavioral patterns.

Findings

Infection times follow a lognormal distribution.

Experimental data supports the lognormal model.

Resembles known human and contagious process distributions.

Abstract

The infection times of individuals in online information spread such as the inter-arrival time of Twitter messages or the propagation time of news stories on a social media site can be explained through a convolution of lognormally distributed observation and reaction times of the individual participants. Experimental measurements support the lognormal shape of the individual contributing processes, and have resemblance to previously reported lognormal distributions of human behavior and contagious processes.