Seeing the forest for the trees: New approaches to forecasting cascades

Siddharth Krishnan, Patrick Butler, Ravi Tandon, Jure Leskovec, Naren Ramakrishnan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations


Cascades are a popular construct to observe and study in- formation propagation (or diffusion) in social media such as Twitter. and are defined using notions of influence, activity, or discourse commonality (e.g., hashtags). While these notions of cascades lead to different perspectives, primarily cascades are modeled as trees. We argue in this paper an alternative viewpoint of cascades as forests (of trees) which yields a richer vocabulary of features to understand information propagation. We develop a framework to extract forests and analyze their growth by studying their evolution at the tree-level and at the node-level. Moreover, we demonstrate how the structural features of forests, properties of the underlying network, and temporal features of the cascades provide significant predictive value in forecasting the future trajectory of both size and shape of forests. We observe that the forecasting performance increases with observations, that the temporal features are highly indicative of cascade size, and that the features extracted from the underlying connected graph best forecast the shape of the cascade.

Original languageEnglish (US)
Title of host publicationWebSci 2016 - Proceedings of the 2016 ACM Web Science Conference
PublisherAssociation for Computing Machinery, Inc
Number of pages10
ISBN (Electronic)9781450342087
StatePublished - May 22 2016
Event8th ACM Web Science Conference, WebSci 2016 - Hannover, Germany
Duration: May 22 2016May 25 2016

Publication series

NameWebSci 2016 - Proceedings of the 2016 ACM Web Science Conference


Conference8th ACM Web Science Conference, WebSci 2016

ASJC Scopus subject areas

  • Computer Networks and Communications


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