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Self-Organized Criticality on Twitter: Phenomenological Theory and Empirical Investigation Based on Data Analysis Results

Author

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  • Andrey Dmitriev
  • Victor Dmitriev
  • Stepan Balybin

Abstract

Recently, there has been an increasing number of empirical evidence supporting the hypothesis that spread of avalanches of microposts on social networks, such as Twitter, is associated with some sociopolitical events. Typical examples of such events are political elections and protest movements. Inspired by this phenomenon, we built a phenomenological model that describes Twitter’s self-organization in a critical state. An external manifestation of this condition is the spread of avalanches of microposts on the network. The model is based on a fractional three-parameter self-organization scheme with stochastic sources. It is shown that the adiabatic mode of self-organization in a critical state is determined by the intensive coordinated action of a relatively small number of network users. To identify the critical states of the network and to verify the model, we have proposed a spectrum of three scaling indicators of the observed time series of microposts.

Suggested Citation

  • 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.
    • Handle: RePEc:hin:complx:8750643
    DOI: 10.1155/2019/8750643
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    References listed on IDEAS

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    1. James P. Gleeson & Rick Durrett, 2017. "Temporal profiles of avalanches on networks," Nature Communications, Nature, vol. 8(1), pages 1-13, December.
    2. Anna D. Broido & Aaron Clauset, 2019. "Scale-free networks are rare," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    3. Olemskoi, Alexander I. & Khomenko, Alexei V. & Kharchenko, Dmitrii O., 2003. "Self-organized criticality within fractional Lorenz scheme," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 323(C), pages 263-293.
    4. 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.
    5. Chengcheng Shao & Giovanni Luca Ciampaglia & Onur Varol & Kai-Cheng Yang & Alessandro Flammini & Filippo Menczer, 2018. "The spread of low-credibility content by social bots," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    6. 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.
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    Cited by:

    1. Tadić, Bosiljka & Mitrović Dankulov, Marija & Melnik, Roderick, 2023. "Evolving cycles and self-organised criticality in social dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).

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