Do Cascades Recur?

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.

Emerging trends in evolving networks: Recent behaviour dominant and non-dominant model

“…Collective behaviours are more important in many domain such as finance, It may be possible in presence of popular nodes its strength forgotten. Researchers have also found that in network such as paper citation it may not get attention for long time (Ke et al 2015), some gain popularity many times (Cheng et al 2016). In all the cases we think as also been proven that recent activity of the node is one of the important feature of node for future activity (Gleeson et al 2014), (ZENG et al 2013).…”

Metrics for Temporal Text Networks

“…A similar model to represent temporal interactions is the contact sequence [6,4] model, which expresses temporal networks as a set of directed edges (called contacts) during a finite span of time. While this model has been successfully used to study spreading processes of information [10,1] or the structural evolution of social networks [17,26,8], the model ignores the role of the content of the messages.…”

Foundations of Temporal Text Networks

“…Contact sequences have been typically used to study information spreading [35,38], and existing concepts such as motifs and triadic closure have been re-defined to study the evolving structure of these networks [36,37,39].…”