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Scientific citations and Facebook shares follow the same distribution patterns, suggesting similar underlying growth and selection mechanisms. This research models citation dynamics, predicting a future average of 4 citations per publication.

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Area of Science:

  • Bibliometrics
  • Social Network Analysis
  • Statistical Physics

Background:

  • Scientific citation distributions exhibit complex patterns influenced by various selection criteria.
  • Social media metrics, like Facebook shares, also show distinct distribution characteristics.

Purpose of the Study:

  • To investigate if scientific citation distributions and social media popularity metrics share common underlying statistical properties.
  • To develop a model explaining the observed distributions and predict future trends in scientific impact.

Main Methods:

  • Analysis of publication citation data from PubMed across different selection rules (author, topic, institution, country, journal).
  • Comparison of citation distributions with Facebook post share distributions.
  • Application of a master-equation approach incorporating exponential publication growth and preferential selection.

Main Results:

  • Citation distributions, when normalized by their mean, collapse onto a single universal curve.
  • Facebook post share distributions exhibit identical rescaling behavior to scientific citation distributions.
  • A Tsallis-Pareto distribution accurately models the observed statistics, driven by exponential growth and preferential selection.

Conclusions:

  • Scientific citations and social media engagement metrics are governed by statistically similar social environments and selection mechanisms.
  • The developed model provides a robust framework for understanding citation dynamics.
  • Future trends predict an exponential relaxation of average citations per scientific publication to approximately 4.