Branching process descriptions of information cascades on Twitter

James P. Gleeson, Tomokatsu Onaga, Peter Fennell, James Cotter, Raymond Burke, David J.P. O'Sullivan

Research output: Contribution to journalArticlepeer-review

Abstract

A detailed analysis of Twitter-based information cascades is performed, and it is demonstrated that branching process hypotheses are approximately satisfied. Using a branching process framework, models of agent-to-agent transmission are compared to conclude that a limited attention model better reproduces the relevant characteristics of the data than the more common independent cascade model. Existing and new analytical results for branching processes are shown to match well to the important statistical characteristics of the empirical information cascades, thus demonstrating the power of branching process descriptions for understanding social information spreading.

Original languageEnglish
Article numbercnab002
JournalJournal of Complex Networks
Volume8
Issue number6
DOIs
Publication statusPublished - 2020 Dec 1

Keywords

  • Branching processes
  • Cascades
  • Networks

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Management Science and Operations Research
  • Control and Optimization
  • Computational Mathematics
  • Applied Mathematics

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