  Modeling and predicting the popularity of online content is a significant
problem for the practice of information dissemination, advertising, and
consumption. Recent work analyzing massive datasets advances our understanding
of popularity, but one major gap remains: To precisely quantify the
relationship between the popularity of an online item and the external
promotions it receives. This work supplies the missing link between exogenous
inputs from public social media platforms, such as Twitter, and endogenous
responses within the content platform, such as YouTube. We develop a novel
mathematical model, the Hawkes intensity process, which can explain the complex
popularity history of each video according to its type of content, network of
diffusion, and sensitivity to promotion. Our model supplies a prototypical
description of videos, called an endo-exo map. This map explains popularity as
the result of an extrinsic factor - the amount of promotions from the outside
world that the video receives, acting upon two intrinsic factors - sensitivity
to promotion, and inherent virality. We use this model to forecast future
popularity given promotions on a large 5-months feed of the most-tweeted
videos, and found it to lower the average error by 28.6% from approaches based
on popularity history. Finally, we can identify videos that have a high
potential to become viral, as well as those for which promotions will have
hardly any effect.
