t h e e A s e o F producing online content highlights
the problem of predicting how much attention any
of it will ultimately receive. Research shows that user
attention9 is allocated in a rather asymmetric way, with
most content getting only some views and downloads,
whereas a few receive the most attention. While it
is possible to predict the distribution of attention
over many items, it is notably dif cult to predict the
amount that will be devoted over time to any given
item. We solve this problem here, illustrating our
approach with data collected from the portals Digg
(http://digg.com) and YouTube (http://youtube.com),
two well-known examples of popular content-sharing-
and- ltering services.
the problem of predicting how much attention any
of it will ultimately receive. Research shows that user
attention9 is allocated in a rather asymmetric way, with
most content getting only some views and downloads,
whereas a few receive the most attention. While it
is possible to predict the distribution of attention
over many items, it is notably dif cult to predict the
amount that will be devoted over time to any given
item. We solve this problem here, illustrating our
approach with data collected from the portals Digg
(http://digg.com) and YouTube (http://youtube.com),
two well-known examples of popular content-sharing-
and- ltering services.