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Everyday the Same Picture: Popularity and Content Diversity

  • Alessandro Bessi
  • Fabiana Zollo
  • Michela Del Vicario
  • Antonio Scala
  • Fabio Petroni
  • Bruno GonçcalvesEmail author
  • Walter Quattrociocchi
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Facebook is flooded by diverse and heterogeneous content, from kittens up to music and news, passing through satirical and funny stories. Each piece of that vivid production reflects the heterogeneity of the underlying social background and provides sometimes interesting opportunities for the study of social dynamics. Indeed, in Facebook we found an interesting case: a page having more than 40 K followers that every day posts the same picture of a popular Italian singer. We use such a peculiar page as a baseline for the study and modeling of the relationship between content heterogeneity and popularity. In particular, we perform a comparative analysis of information consumption patterns with respect to pages posting heterogeneous content (science and conspiracy news). We conclude the paper by introducing a model mimicking users selection preferences accounting for the heterogeneity of contents.

Keywords

Probability Density Function Probability Density Function Beta Distribution Online Social Network Bipartite Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Funding for this work was provided by EU FET project MULTIPLEX nr. 317532 and SIMPOL nr. 610704. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We want to thank Prof. Guido Caldarelli for precious insights and contribution on the data analysis. Special thanks to Josif Stalin, Stefano Alpi, Michele Degani for giving access to the Facebook page of La stessa foto di Toto Cutugno ogni giorno. Bruno Gonçalves thanks the Moore and Sloan Foundations for support as part of the Moore-Sloan Data Science Environment at NYU.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Alessandro Bessi
    • 1
  • Fabiana Zollo
    • 2
  • Michela Del Vicario
    • 2
  • Antonio Scala
    • 3
  • Fabio Petroni
    • 4
  • Bruno Gonçcalves
    • 5
    Email author
  • Walter Quattrociocchi
    • 2
  1. 1.Information Sciences Institute, University of Southern CaliforniaLos AngelesUSA
  2. 2.IMT Institute for Advanced StudiesLuccaItaly
  3. 3.ISC CNRRomeItaly
  4. 4.Sapienza University of RomeRomeItaly
  5. 5.Center for Data ScienceNew York UniversityNew YorkUSA

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