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Gabor Szabo and Bernardo A. Huberman
Social Computing Laboratory, HP Labs
Abstract
We present a method for accurately predicting the long time
popularity of online content from early measurements of
user’s 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 pre-
dict 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become outdated, while Youtube videos are still found long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.
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