Modeling cascade formation in Twitter amidst mentions and retweets

  • Soumajit PramanikEmail author
  • Qinna Wang
  • Maximilien Danisch
  • Jean-Loup Guillaume
  • Bivas Mitra
Original Article


This paper presents an analytical framework for cascade formation considering both retweet and mentioning activities into account. We introduce two mention strategies (a) random mention and (b) smart mention to model the mention preferences of the users. The proposed framework \({\mathcal {C}}^M_F\) analytically computes the cascade size, depicting tweet popularity and discovers the presence of a critical retweet rate, under which mentioning in a tweet significantly helps in cascade formation. We validate the proposed framework with the help of Monte Carlo simulation; we demonstrate the generality of the framework taking both empirical and synthetic follower networks into consideration. This framework proves the elegance of smart mention strategy in boosting tweet popularity, specially in the low retweeting environment.


Multiplex network Epidemic models Information diffusion Twitter 



This work has been partially supported by the SAP Labs India Doctoral Fellowship program, DST—CNRS funded Indo—French collaborative project ‘Evolving Communities and Information Spreading’ and French National Research Agency contract CODDDE ANR-13-CORD-0017-01.


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Copyright information

© Springer-Verlag GmbH Austria 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIIT KharagpurKharagpurIndia
  2. 2.Sorbonne Universités, UPMC Univ Paris 06, CNRS, LIP6 UMR 7606ParisFrance
  3. 3.Telecom Paris TechParisFrance
  4. 4.L3IUniversity of La RochelleLa RochelleFrance

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