Physica A 415 (2014) 87–94 Contents lists available at ScienceDirect Physica A journal homepage: www.elsevier.com/locate/physa Viral information propagation in the Digg online social network Mark Freeman a , James McVittie b , Iryna Sivak c,1 , Jianhong Wu d,∗ a Program for Evolutionary Dynamics, Harvard College, One Brattle Square, Suite 6, Cambridge, MA 02138-3758, USA b Department of Statistics, University of Toronto, 100 St. George St., Toronto, Ontario, Canada c Department of Mathematics, Taras Shevchenko National University of Kyiv, Volodymyrska st. 64, 01601, Kyiv, Ukraine d Laboratory for Industrial and Applied Mathematics, York University, 4700 Keele Street, Toronto, Ontario, Canada, M3J 1P3 highlights • We propose and analyze an epidemiological model for information propagation in online social network. • We characterize peak timing, turning point, viral period, and final size of the number of votes. • There are significant similarity and difference between information propagation in OSNs differs from disease spread in populations. • Simple dynamic models can provide accurate prediction of information propagation in OSNs. article info Article history: Received 27 November 2013 Received in revised form 2 June 2014 Available online 16 July 2014 Keywords: Online social network Digg network Information spread Mathematical epidemiology Richards model abstract We propose the use of a variant of the epidemiological SIR model to accurately describe the diffusion of online content over the online social network Digg.com. We examine the qualitative properties of our viral information propagation model, demonstrate the model’s applications to social media spread in online social networks with particular focus on accu- rately predicting user voting behavior over a period of 50 h. The model allows us to charac- terize the peak time, turning point, viral period and final size (total number of votes), and gives much improved prediction of user voting behaviors than other established models. © 2014 Elsevier B.V. All rights reserved. 1. Introduction Everyday in online social networks (OSNs), thousands of users post news articles, videos, photos etc. which become visible to their connected users as new online content. As most of these forms of media never spread to a wide audience from the sources, many users are influenced by their networking. The causes and dynamics by which information proliferates throughout OSNs are still poorly understood. A greater comprehension of the mathematics underlying the spread of information in OSNs would have important applications for advertisers seeking to wage more effective online marketing campaigns and may enable a more rapid spread of information over OSNs in the aftermath of political crises or natural disasters. The focus of this article is the OSN Digg.com (DOSN). In this network, users are able to post content to a personal web page, vote for (‘‘digg’’) or against (‘‘bury’’) this content and share the content with users to whom they are connected. There ∗ Corresponding author. Tel.: +1 4167365356. E-mail address: wujh@mathstat.yorku.ca (J. Wu). 1 Current address: Centre for Complexity Science, University of Warwick, Coventry CV4 7AL, United Kingdom. http://dx.doi.org/10.1016/j.physa.2014.06.011 0378-4371/© 2014 Elsevier B.V. All rights reserved.