  Can we predict the future popularity of a song, movie or tweet? Recent work
suggests that although it may be hard to predict an item's popularity when it
is first introduced, peeking into its early adopters and properties of their
social network makes the problem easier. We test the robustness of such claims
by using data from social networks spanning music, books, photos, and URLs. We
find a stronger result: not only do predictive models with peeking achieve high
accuracy on all datasets, they also generalize well, so much so that models
trained on any one dataset perform with comparable accuracy on items from other
datasets.
  Though practically useful, our models (and those in other work) are
intellectually unsatisfying because common formulations of the problem, which
involve peeking at the first small-k adopters and predicting whether items end
up in the top half of popular items, are both too sensitive to the speed of
early adoption and too easy. Most of the predictive power comes from looking at
how quickly items reach their first few adopters, while for other features of
early adopters and their networks, even the direction of correlation with
popularity is not consistent across domains. Problem formulations that examine
items that reach k adopters in about the same amount of time reduce the
importance of temporal features, but also overall accuracy, highlighting that
we understand little about why items become popular while providing a context
in which we might build that understanding.
