  Cascades of information-sharing are a primary mechanism by which content
reaches its audience on social media, and an active line of research has
studied how such cascades, which form as content is reshared from person to
person, develop and subside. In this paper, we perform a large-scale analysis
of cascades on Facebook over significantly longer time scales, and find that a
more complex picture emerges, in which many large cascades recur, exhibiting
multiple bursts of popularity with periods of quiescence in between. We
characterize recurrence by measuring the time elapsed between bursts, their
overlap and proximity in the social network, and the diversity in the
demographics of individuals participating in each peak. We discover that
content virality, as revealed by its initial popularity, is a main driver of
recurrence, with the availability of multiple copies of that content helping to
spark new bursts. Still, beyond a certain popularity of content, the rate of
recurrence drops as cascades start exhausting the population of interested
individuals. We reproduce these observed patterns in a simple model of content
recurrence simulated on a real social network. Using only characteristics of a
cascade's initial burst, we demonstrate strong performance in predicting
whether it will recur in the future.
