The Bursty Dynamics of the Twitter Information Network

TL;DR

This paper investigates how user posting, sharing, and connection changes on Twitter interact, revealing bursty patterns in network evolution driven by information cascades and real-world events, and proposes a predictive model.

Contribution

It introduces a comprehensive analysis of the interplay between information diffusion and network structural changes, and develops a model to predict bursty network dynamics based on content spread.

Findings

Network structure changes occur in steady states interrupted by bursts.

Information cascades often trigger sudden network restructuring.

The proposed model accurately predicts bursts caused by diffusion events.

Abstract

In online social media systems users are not only posting, consuming, and resharing content, but also creating new and destroying existing connections in the underlying social network. While each of these two types of dynamics has individually been studied in the past, much less is known about the connection between the two. How does user information posting and seeking behavior interact with the evolution of the underlying social network structure? Here, we study ways in which network structure reacts to users posting and sharing content. We examine the complete dynamics of the Twitter information network, where users post and reshare information while they also create and destroy connections. We find that the dynamics of network structure can be characterized by steady rates of change, interrupted by sudden bursts. Information diffusion in the form of cascades of post re-sharing often creates such sudden bursts of new connections, which significantly change users' local network structure. These bursts transform users' networks of followers to become structurally more cohesive as well as more homogenous in terms of follower interests. We also explore the effect of the information content on the dynamics of the network and find evidence that the appearance of new topics and real-world events can lead to significant changes in edge creations and deletions. Lastly, we develop a model that quantifies the dynamics of the network and the occurrence of these bursts as a function of the information spreading through the network. The model can successfully predict which information diffusion events will lead to bursts in network dynamics.