Bernardo A. Huberman:

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are shown to be due to differences in how content is consumed on the two portals: Digg stories quickly become outdated, while Youtube videos are still found long after they are initially submitted to the portal.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.

See also, from February:

We analyze the role that popularity and novelty play in attracting the attention of users to dynamic websites.

From 2007:

Novelty within groups decays with a stretched-exponential law, suggesting the existence of a natural time scale over which attention fades.

From 2002:

The amount of money that participants were willing to trade off against status corresponded to the power distance index of the respective culture. The power distance index of a culture has been shown to be correlated with the importance and acceptance of status symbols in that culture. Finally, the amount of status seeking observed was different among men and women, an intriguing observation that deserves further work.

Predicting the popularity of online content