Do Cascades Recur?

TL;DR

This study reveals that social media cascades often recur with multiple bursts over time, influenced by initial virality and content availability, and introduces a model to predict recurrence based on early cascade features.

Contribution

The paper provides the first large-scale analysis of cascade recurrence on Facebook, characterizing multiple bursts and developing a predictive model using early cascade data.

Findings

Large cascades often recur with multiple bursts.

Recurrence is driven by initial virality and content copies.

Prediction of recurrence is effective using early cascade features.

Abstract

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.