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Do Cascades Recur?

Published: 11 April 2016 Publication History

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.

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cover image ACM Other conferences
WWW '16: Proceedings of the 25th International Conference on World Wide Web
April 2016
1482 pages
ISBN:9781450341431

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  • IW3C2: International World Wide Web Conference Committee

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 11 April 2016

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Author Tags

  1. cascade prediction
  2. content recurrence
  3. information diffusion
  4. memes
  5. virality

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  • Research-article

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  • NSF

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WWW '16
Sponsor:
  • IW3C2
WWW '16: 25th International World Wide Web Conference
April 11 - 15, 2016
Québec, Montréal, Canada

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WWW '16 Paper Acceptance Rate 115 of 727 submissions, 16%;
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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