Understanding and forecasting lifecycle events in information cascades

TL;DR

This paper analyzes the lifecycle of information cascades on social networks, focusing on identifying and forecasting the peak growth and decline periods using network topology measures and Granger causality.

Contribution

It introduces a holistic approach to understanding cascade dynamics by analyzing key lifecycle events and extends causality models to predict their timing based on network structure.

Findings

Entropy of nodal degree causally influences cascade events in 93.95% of cases.

Network topology measures significantly impact cascade growth and decline.

The extended VAR model effectively forecasts lifecycle event timings.

Abstract

Most social network sites allow users to reshare a piece of information posted by a user. As time progresses, the cascade of reshares grows, eventually saturating after a certain time period. While previous studies have focused heavily on one aspect of the cascade phenomenon, specifically predicting when the cascade would go viral, in this paper, we take a more holistic approach by analyzing the occurrence of two events within the cascade lifecycle - the period of maximum growth in terms of surge in reshares and the period where the cascade starts declining in adoption. We address the challenges in identifying these periods and then proceed to make a comparative analysis of these periods from the perspective of network topology. We study the effect of several node-centric structural measures on the reshare responses using Granger causality which helps us quantify the significance of the network measures and understand the extent to which the network topology impacts the growth dynamics. This evaluation is performed on a dataset of 7407 cascades extracted from the Weibo social network. Using our causality framework, we found that an entropy measure based on nodal degree causally affects the occurrence of these events in 93.95% of cascades. Surprisingly, this outperformed clustering coefficient and PageRank which we hypothesized would be more indicative of the growth dynamics based on earlier studies. We also extend the Granger-causality Vector Autoregression (VAR) model to forecast the times at which the events occur in the cascade lifecycle.