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Explaining the emergence of online popularity through a model of information diffusion

Author

Listed:
  • António Fonseca

    (Instituto Universitário de Lisboa (ISCTE - IUL))

  • Jorge Louçã

    (Instituto Universitário de Lisboa (ISCTE - IUL))

Abstract

This paper proposes a new formal modeling approach to popularity dynamics based on a generic notion of message propagation within society. The approach is demonstrated with two original models of information diffusion. These are a branching model of popularity and a epidemic model of popularity. The first is based on the principles of a branching process, while the second emulates an epidemic equation with a specific infection rate. This allows us to consider the replication phenomena on information diffusion. The approach is validated using a very large dataset collected online that involves keywords in blogs and hashtags on Twitter. Our main results point to an overall good fit of both models, both when the process of popularity grows and when it decays. This is due to endogenous information transfer, as in an epidemic process, but also when the process is initially triggered by an external event. Overall, on balance, our models confirm that popularity builds through message diffusion, which is of the multiplicative kind.

Suggested Citation

  • António Fonseca & Jorge Louçã, 2018. "Explaining the emergence of online popularity through a model of information diffusion," Computational and Mathematical Organization Theory, Springer, vol. 24(2), pages 169-187, June.
    • Handle: RePEc:spr:comaot:v:24:y:2018:i:2:d:10.1007_s10588-017-9253-5
    DOI: 10.1007/s10588-017-9253-5
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    1. repec:cup:cbooks:9780511771576 is not listed on IDEAS
    2. Zongyang Ma & Aixin Sun & Gao Cong, 2013. "On predicting the popularity of newly emerging hashtags in Twitter," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(7), pages 1399-1410, July.
    3. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331.
    4. Zongyang Ma & Aixin Sun & Gao Cong, 2013. "On predicting the popularity of newly emerging hashtags in Twitter," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(7), pages 1399-1410, July.
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    Cited by:

    1. Raúl M. Ortiz-Gaona & Marcos Postigo-Boix & José L. Melús-Moreno, 2021. "Extent prediction of the information and influence propagation in online social networks," Computational and Mathematical Organization Theory, Springer, vol. 27(2), pages 195-230, June.

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