What Stops Social Epidemics?

TL;DR

This paper investigates why social media cascades often remain small despite rapid initial spread, revealing that network clustering and reduced susceptibility upon repeated exposure limit epidemic sizes.

Contribution

It identifies how network clustering and decreased likelihood of sharing after multiple exposures jointly constrain social epidemic sizes, a novel insight into social contagion dynamics.

Findings

Highly clustered networks lower epidemic thresholds.

Repeated exposure reduces individuals' likelihood to share.

Social epidemics on Digg are severely limited in size.

Abstract

Theoretical progress in understanding the dynamics of spreading processes on graphs suggests the existence of an epidemic threshold below which no epidemics form and above which epidemics spread to a significant fraction of the graph. We have observed information cascades on the social media site Digg that spread fast enough for one initial spreader to infect hundreds of people, yet end up affecting only 0.1% of the entire network. We find that two effects, previously studied in isolation, combine cooperatively to drastically limit the final size of cascades on Digg. First, because of the highly clustered structure of the Digg network, most people who are aware of a story have been exposed to it via multiple friends. This structure lowers the epidemic threshold while moderately slowing the overall growth of cascades. In addition, we find that the mechanism for social contagion on Digg points to a fundamental difference between information spread and other contagion processes: despite multiple opportunities for infection within a social group, people are less likely to become spreaders of information with repeated exposure. The consequences of this mechanism become more pronounced for more clustered graphs. Ultimately, this effect severely curtails the size of social epidemics on Digg.