Predicting the Popularity of Online Content

10 Pages Posted: 5 Nov 2008

See all articles by Gabor Szabo

Gabor Szabo

Hewlett-Packard Laboratories, Palo Alto

Bernardo A. Huberman


Date Written: November 4, 2008


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are shown to be due to differences in how content is consumed on the two portals: Digg stories quickly become outdated, while Youtube videos are still found long after they are initially submitted to the portal.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

Suggested Citation

Szabo, Gabor and Huberman, Bernardo A., Predicting the Popularity of Online Content (November 4, 2008). Available at SSRN: or

Gabor Szabo

Hewlett-Packard Laboratories, Palo Alto ( email )

1501 Page Mill Road
Palo Alto, CA 94301
United States

Bernardo A. Huberman (Contact Author)

CableLabs ( email )

400 W California Ave
Sunnyvale, CA 94086
United States

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