Social Dynamics of Digg

TL;DR

This paper models how social dynamics influence content popularity on Digg, distinguishing between friend-based and community interest, and predicts story success from early user reactions.

Contribution

It introduces a stochastic model that separates content visibility and interestingness, enabling prediction of story popularity and informing content display design.

Findings

Interest varies widely among users.

Early reactions can predict long-term popularity.

Model helps evaluate content display strategies.

Abstract

Online social media provide multiple ways to find interesting content. One important method is highlighting content recommended by user's friends. We examine this process on one such site, the news aggregator Digg. With a stochastic model of user behavior, we distinguish the effects of the content visibility and interestingness to users. We find a wide range of interest and distinguish stories primarily of interest to a users' friends from those of interest to the entire user community. We show how this model predicts a story's eventual popularity from users' early reactions to it, and estimate the prediction reliability. This modeling framework can help evaluate alternative design choices for displaying content on the site.