http://arxiv.org/abs/0904.0016 Fans, the law of web surfing and users' interests combine to promote and rate stories on Digg. We describe a general stochastic processes-based approach to modeling user-contributory web sites, where users create, rate and share content. These models describe aggregate measures of activity and how they arise from simple models of individual users. This approach provides a tractable method to understand user activity on the web site and how this activity depends on web site design choices, especially the choice of what information about other users' behaviors is shown to each user. We illustrate this modeling approach in the context of user-created content on the news rating site Digg. (In Proc. of the 3rd Int'l AAAI Conference on Weblogs and Social Media) Tad Hogg and Kristina Lerman 2009-04-02 00:39:00 ICWSM digg social media social networks online content popularity http://www.hpl.hp.com/research/scl/papers/predictions popularity, youtube, digg, attention, predicting future downloads. attention youtube popularity social media prediction online content We present a method for accurately predicting the long time popularity of online content from early measurements of user access. Using two content sharing portals, Youtube and Digg, we show that by modeling the accrual of views and votes on content offered by these services we can predict the long-term dynamics of individual submissions from initial data. In the case of Digg, measuring access to given stories during the first two hours allows us to forecast their popularity 30 days ahead with remarkable accuracy, while downloads of Youtube videos need to be followed for 10 days to attain the same performance. The differing time scales of the predictions are shown to be due to differences in how content is consumed on the two portals: Digg stories quickly become outdated, while Youtube videos are still found long after they are initially submitted to the portal. We show that predictions are more accurate for submissions for which attention decays quickly, whereas predictions for evergreen content will be prone to larger errors. Gabor Szabo and Bernardo A. Huberman 2008-11-03 15:27:00