Popularity of content in social media is unequally distributed, with some
items receiving a disproportionate share of attention from users. Predicting
which newly-submitted items will become popular is critically important for
both companies that host social media sites and their users. Accurate and
timely prediction would enable the companies to maximize revenue through
differential pricing for access to content or ad placement. Prediction would
also give consumers an important tool for filtering the ever-growing amount of
content. Predicting popularity of content in social media, however, is
challenging due to the complex interactions among content quality, how the
social media site chooses to highlight content, and influence among users.
While these factors make it difficult to predict popularity \emph{a priori}, we
show that stochastic models of user behavior on these sites allows predicting
popularity based on early user reactions to new content. By incorporating
aspects of the web site design, such models improve on predictions based on
simply extrapolating from the early votes. We validate this claim on the social
news portal Digg using a previously-developed model of social voting based on
the Digg user interface.