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Modeling cascade formation in Twitter amidst mentions and retweets

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Abstract

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.

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Notes

  1. Notably, in this section, we perform experiments on both hashtags and tweets. This is because both ‘hashtag’ and ‘tweet’ are commonly used as an unit of information in Twitter and the propagation dynamics can be assumed to be mostly identical for both of them.

  2. http://dfreelon.org/2012/02/11/arab-spring-twitter-data-now-available-sort-of/.

  3. As inspired from the datastudy (Sect. 2.3), we keep two separate retweet rates for follow and mention.

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Acknowledgements

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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Authors and Affiliations

  1. Department of Computer Science and Engineering, IIT Kharagpur, Kharagpur, India

    Soumajit Pramanik & Bivas Mitra

  2. Sorbonne Universités, UPMC Univ Paris 06, CNRS, LIP6 UMR 7606, 4 place Jussieu, 75005, Paris, France

    Qinna Wang

  3. Telecom Paris Tech, Paris, France

    Maximilien Danisch

  4. L3I, University of La Rochelle, La Rochelle, France

    Jean-Loup Guillaume

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  1. Soumajit Pramanik
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  2. Qinna Wang
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  5. Bivas Mitra
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Correspondence to Soumajit Pramanik.

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Pramanik, S., Wang, Q., Danisch, M. et al. Modeling cascade formation in Twitter amidst mentions and retweets. Soc. Netw. Anal. Min. 7, 41 (2017). https://doi.org/10.1007/s13278-017-0462-1

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