Social Contagion: An Empirical Study of Information Spread on Digg and Twitter Follower Graphs

TL;DR

This study empirically analyzes how news stories spread through follower networks on Digg and Twitter, revealing insights into the dynamics of information dissemination on social media platforms.

Contribution

It provides the first direct analysis of follower graphs and cascades on Digg and Twitter, clarifying how network structure influences information spread.

Findings

Differences in cascade sizes between Digg and Twitter

Network structure impacts information flow dynamics

Empirical data clarifies real-world diffusion patterns

Abstract

Social networks have emerged as a critical factor in information dissemination, search, marketing, expertise and influence discovery, and potentially an important tool for mobilizing people. Social media has made social networks ubiquitous, and also given researchers access to massive quantities of data for empirical analysis. These data sets offer a rich source of evidence for studying dynamics of individual and group behavior, the structure of networks and global patterns of the flow of information on them. However, in most previous studies, the structure of the underlying networks was not directly visible but had to be inferred from the flow of information from one individual to another. As a result, we do not yet understand dynamics of information spread on networks or how the structure of the network affects it. We address this gap by analyzing data from two popular social news sites. Specifically, we extract follower graphs of active Digg and Twitter users and track how interest in news stories cascades through the graph. We compare and contrast properties of information cascades on both sites and elucidate what they tell us about dynamics of information flow on networks.