Briefs

Posted by Josh Lowensohn

November 13, 2008. Bernardo Huberman, Hewlett-Packard's director of the HP Social Computing lab, and fellow researcher Gabor Szabo have published a highly detailed report (PDF) on "predicting the popularity of online content."

Focusing on content submitted and popularized on popular social sites Digg.com and Google's YouTube, the two concocted not one but three ways to predict how much traffic and overall user interaction a story or submitted video will receive well after it hits its initial popularity.

To do this the pair kept an eye on 7,146 videos from YouTube's recently added section, and every digg from registered digg users between July 1, 2007, to December 18, 2007. From this data, they found that stories on Digg got more votes and views during peak traffic hours than those at nights and on weekends (duh), and that YouTube videos tended to get more and more views a month into being submitted--and in many cases well beyond the initial 30-day evaluation.

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