Branching Dynamics of Viral Information Spreading

TL;DR

This paper analyzes the complex, non-Markovian branching dynamics of viral information spreading in real campaigns, revealing a model that captures human behavioral variability and predicts propagation features.

Contribution

It introduces a two-step Bellman-Harris Branching Process model that generalizes static models to better explain viral information diffusion driven by human decisions.

Findings

Information spreading follows non-Markovian branching dynamics.

The model accurately predicts propagation features under the 'tipping-point'.

The approach enables better prediction and management of viral diffusion.

Abstract

Despite its importance for rumors or innovations propagation, peer-to-peer collaboration, social networking or Marketing, the dynamics of information spreading is not well understood. Since the diffusion depends on the heterogeneous patterns of human behavior and is driven by the participants' decisions, its propagation dynamics shows surprising properties not explained by traditional epidemic or contagion models. Here we present a detailed analysis of our study of real Viral Marketing campaigns where tracking the propagation of a controlled message allowed us to analyze the structure and dynamics of a diffusion graph involving over 31,000 individuals. We found that information spreading displays a non-Markovian branching dynamics that can be modeled by a two-step Bellman-Harris Branching Process that generalizes the static models known in the literature and incorporates the high variability of human behavior. It explains accurately all the features of information propagation under the "tipping-point" and can be used for prediction and management of viral information spreading processes.