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Universality, criticality and complexity of information propagation in social media

Author

Listed:
  • Daniele Notarmuzi

    (Luddy School of Informatics, Computing, and Engineering, Indiana University)

  • Claudio Castellano

    (Istituto dei Sistemi Complessi (ISC-CNR)
    Centro Ricerche Enrico Fermi)

  • Alessandro Flammini

    (Luddy School of Informatics, Computing, and Engineering, Indiana University)

  • Dario Mazzilli

    (Luddy School of Informatics, Computing, and Engineering, Indiana University
    Centro Ricerche Enrico Fermi)

  • Filippo Radicchi

    (Luddy School of Informatics, Computing, and Engineering, Indiana University)

Abstract

Statistical laws of information avalanches in social media appear, at least according to existing empirical studies, not robust across systems. As a consequence, radically different processes may represent plausible driving mechanisms for information propagation. Here, we analyze almost one billion time-stamped events collected from several online platforms – including Telegram, Twitter and Weibo – over observation windows longer than ten years, and show that the propagation of information in social media is a universal and critical process. Universality arises from the observation of identical macroscopic patterns across platforms, irrespective of the details of the specific system at hand. Critical behavior is deduced from the power-law distributions, and corresponding hyperscaling relations, characterizing size and duration of avalanches of information. Statistical testing on our data indicates that a mixture of simple and complex contagion characterizes the propagation of information in social media. Data suggest that the complexity of the process is correlated with the semantic content of the information that is propagated.

Suggested Citation

  • Daniele Notarmuzi & Claudio Castellano & Alessandro Flammini & Dario Mazzilli & Filippo Radicchi, 2022. "Universality, criticality and complexity of information propagation in social media," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28964-8
    DOI: 10.1038/s41467-022-28964-8
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    References listed on IDEAS

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    1. Weihua Li & Skyler J Cranmer & Zhiming Zheng & Peter J Mucha, 2019. "Infectivity enhances prediction of viral cascades in Twitter," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-12, April.
    2. Quentin Michard & Jean-Philippe Bouchaud, 2005. "Theory of collective opinion shifts: from smooth trends to abrupt swings," Science & Finance (CFM) working paper archive 500060, Science & Finance, Capital Fund Management.
    3. Q. Michard & J.-P. Bouchaud, 2005. "Theory of collective opinion shifts: from smooth trends to abrupt swings," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 47(1), pages 151-159, September.
    4. Albert-László Barabási, 2005. "The origin of bursts and heavy tails in human dynamics," Nature, Nature, vol. 435(7039), pages 207-211, May.
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    Cited by:

    1. Rupert Kiddle & Petter Törnberg & Damian Trilling, 2024. "Network toxicity analysis: an information-theoretic approach to studying the social dynamics of online toxicity," Journal of Computational Social Science, Springer, vol. 7(1), pages 305-330, April.

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