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Emergent User Behavior on Twitter Modelled by a Stochastic Differential Equation

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  • Anders Mollgaard
  • Joachim Mathiesen

Abstract

Data from the social-media site, Twitter, is used to study the fluctuations in tweet rates of brand names. The tweet rates are the result of a strongly correlated user behavior, which leads to bursty collective dynamics with a characteristic 1/f noise. Here we use the aggregated "user interest" in a brand name to model collective human dynamics by a stochastic differential equation with multiplicative noise. The model is supported by a detailed analysis of the tweet rate fluctuations and it reproduces both the exact bursty dynamics found in the data and the 1/f noise.

Suggested Citation

  • Anders Mollgaard & Joachim Mathiesen, 2015. "Emergent User Behavior on Twitter Modelled by a Stochastic Differential Equation," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-12, May.
    • Handle: RePEc:plo:pone00:0123876
    DOI: 10.1371/journal.pone.0123876
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    References listed on IDEAS

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    1. Jeff Alstott & Ed Bullmore & Dietmar Plenz, 2014. "powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
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    Cited by:

    1. Andrey Dmitriev & Vasily Kornilov & Svetlana Maltseva, 2018. "Complexity of a Microblogging Social Network in the Framework of Modern Nonlinear Science," Complexity, Hindawi, vol. 2018, pages 1-11, December.
    2. Pan, Jun-Shan & Li, Yuan-Qi & Hu, Han-Ping & Hu, Yong, 2021. "Modeling collective behavior of posting microblogs by stochastic differential equation with jump," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    3. Andrey Dmitriev & Victor Dmitriev & Stepan Balybin, 2019. "Self-Organized Criticality on Twitter: Phenomenological Theory and Empirical Investigation Based on Data Analysis Results," Complexity, Hindawi, vol. 2019, pages 1-16, December.

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