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Social dynamics of Digg

Authors
  • Hogg, Tad1
  • Lerman, Kristina2
  • 1 Institute for Molecular Manufacturing, Palo Alto, CA, USA , Palo Alto (United States)
  • 2 USC Information Sciences Institute, Marina del Rey, CA, USA , Marina del Rey (United States)
Type
Published Article
Journal
EPJ Data Science
Publisher
Springer Berlin Heidelberg
Publication Date
Jun 18, 2012
Volume
1
Issue
1
Identifiers
DOI: 10.1140/epjds5
Source
Springer Nature
Keywords
License
Green

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.

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