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A meritocratic network formation model for the rise of social media influencers

Author

Listed:
  • Nicolò Pagan

    (ETH Zürich
    University of Zürich
    ETH Zürich)

  • Wenjun Mei

    (ETH Zürich
    Peking University)

  • Cheng Li

    (ETH Zürich)

  • Florian Dörfler

    (ETH Zürich)

Abstract

Many of today’s most used online social networks such as Instagram, YouTube, Twitter, or Twitch are based on User-Generated Content (UGC). Thanks to the integrated search engines, users of these platforms can discover and follow their peers based on the UGC and its quality. Here, we propose an untouched meritocratic approach for directed network formation, inspired by empirical evidence on Twitter data: actors continuously search for the best UGC provider. We theoretically and numerically analyze the network equilibria properties under different meeting probabilities: while featuring common real-world networks properties, e.g., scaling law or small-world effect, our model predicts that the expected in-degree follows a Zipf’s law with respect to the quality ranking. Notably, the results are robust against the effect of recommendation systems mimicked through preferential attachment based meeting approaches. Our theoretical results are empirically validated against large data sets collected from Twitch, a fast-growing platform for online gamers.

Suggested Citation

  • Nicolò Pagan & Wenjun Mei & Cheng Li & Florian Dörfler, 2021. "A meritocratic network formation model for the rise of social media influencers," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-27089-8
    DOI: 10.1038/s41467-021-27089-8
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    Cited by:

    1. Bin Zhou & Petter Holme & Zaiwu Gong & Choujun Zhan & Yao Huang & Xin Lu & Xiangyi Meng, 2023. "The nature and nurture of network evolution," Nature Communications, Nature, vol. 14(1), pages 1-8, December.

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