  How many listens will an artist receive on a online radio? How about plays on
a YouTube video? How many of these visits are new or returning users? Modeling
and mining popularity dynamics of social activity has important implications
for researchers, content creators and providers. We here investigate the effect
of revisits (successive visits from a single user) on content popularity. Using
four datasets of social activity, with up to tens of millions media objects
(e.g., YouTube videos, Twitter hashtags or LastFM artists), we show the effect
of revisits in the popularity evolution of such objects. Secondly, we propose
the Phoenix-R model which captures the popularity dynamics of individual
objects. Phoenix-R has the desired properties of being: (1) parsimonious, being
based on the minimum description length principle, and achieving lower root
mean squared error than state-of-the-art baselines; (2) applicable, the model
is effective for predicting future popularity values of objects.
