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

Predictability of Popularity: Gaps between Prediction and Understanding