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Do Cascades Recur?

Do Cascades Recur?

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.

Presented at WWW 2016.

Justin Cheng

April 15, 2016

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  1. Time Popularity Ahmed et al. (2013), Bauckhage et al. (2013),

    Matsubara et al. (2012), Yang & Counts (2010) Prior work: cascades rise, then fall
  2. Studying recurrence on Facebook 5 billion reshares 100 million photos

    76 thousand clusters (Sampled over all of 2014 and de-identified)
  3. Defining recurrence Popularity r Peaks have a minimum absolute/relative height

    rp ≥ h0, rp ≥ m·r *Not a peak Time t r p4 p1 p4 p2 pi pi rp p1 rp p2 rp p4
  4. Defining recurrence Popularity r Peaks are local maxima rp ≥

    max { rj | pi -w ≤ j ≤ pi +w } Time t p1 p4 p2 pi rp p1
  5. Defining recurrence Popularity r Valleys separate peaks rp , rp

    ≥ v · max { rj | pi < j < pi+1 } Time t p1 p4 p2 pi pi+1 rp p1
  6. 0.00 0.25 0.50 0.75 1.00 0 5 10 15 Recurrence

    is common Number of Bursts Empirical CCDF 40% of cascades recur 0.40 2
  7. 0.00 0.25 0.50 0.75 1.00 0 5 10 15 Recurrence

    is common Number of Bursts Empirical CCDF Cascades have 2.3 bursts on average 2.3
  8. Recurrence takes time Days Between 1st and 2nd Burst Empirical

    CCDF 0.00 0.25 0.50 0.75 1.00 0 50 100 150 200
  9. 0.00 0.25 0.50 0.75 1.00 0 50 100 150 200

    Recurrence takes time Days Between 1st and 2nd Burst Empirical CCDF A meme takes an average of 32 days to recur 32
  10. What is recurrence? A cascade recurs when it peaks in

    popularity more than once. Recurrence is common. Recurrence takes time.
  11. Large initial bursts exhaust susceptibles Size of Initial Burst Proportion

    Exposed in Second Burst 0.2 0.4 0.6 0.8 103 104 105 106
  12. Moderate diversity increases recurrence Country Entropy in Initial Burst Mean

    Bursts Gender Entropy in Initial Burst 2.0 2.5 3.0 3.5 0 1 2 3 4 5 1.5 2.0 2.5 3.0 0.2 0.4 0.6 0.8 1.0
  13. New copies can spark recurrence… Recurring cascades are spread out

    over more copies Recurring Non-recurring New copies correlate with recurrence r = 0.66 Introduction of new copies & number of reshares 4x
  14. …but copies aren’t the only cause! Individual copies also recur

    18% of individual copies recur Copies can be traced back to other copies 75% of copies can be attributed
 via the friendship graph
  15. How do cascades recur? Bursts happen in separate parts of

    the network. Moderately viral/diverse content tends to recur. New copies can spark recurrence.
  16. Predicting recurrence Will it recur? Will it be larger? When

    will it recur? Existence Size Time .89 .78 .58 AUC AUC AUC
  17. Future / Related Work • Effect of Multiple Networks, External

 Gruhl et al. (2004), Kumar et al. (2005), Myers & Leskovec (2012) • Improved Models of Recurrence
 Barabasi (2006), Cha et al. (2012), Matsubara et al. (2012) • Other Factors Influencing Recurrence (Seasonality, Sentimentality)
 Altizer et al. (2006), Verdasca et al. (2005)