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Open Access Regular article

Social dynamics of Digg

Tad Hogg1 and Kristina Lerman2*

Author Affiliations

1 Institute for Molecular Manufacturing, Palo Alto, CA, USA

2 USC Information Sciences Institute, Marina del Rey, CA, USA

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EPJ Data Science 2012, 1:5  doi:10.1140/epjds5

Published: 18 June 2012

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.