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Quantifying Social Influence in an Online Cultural Market

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Listed:
  • Coco Krumme
  • Manuel Cebrian
  • Galen Pickard
  • Sandy Pentland

Abstract

We revisit experimental data from an online cultural market in which 14,000 users interact to download songs, and develop a simple model that can explain seemingly complex outcomes. Our results suggest that individual behavior is characterized by a two-step process–the decision to sample and the decision to download a song. Contrary to conventional wisdom, social influence is material to the first step only. The model also identifies the role of placement in mediating social signals, and suggests that in this market with anonymous feedback cues, social influence serves an informational rather than normative role.

Suggested Citation

  • Coco Krumme & Manuel Cebrian & Galen Pickard & Sandy Pentland, 2012. "Quantifying Social Influence in an Online Cultural Market," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-6, May.
    • Handle: RePEc:plo:pone00:0033785
    DOI: 10.1371/journal.pone.0033785
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    References listed on IDEAS

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    1. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    2. Lones Smith & Peter Sorensen, 2000. "Pathological Outcomes of Observational Learning," Econometrica, Econometric Society, vol. 68(2), pages 371-398, March.
    3. Christian Borghesi & Jean-Philippe Bouchaud, 2007. "Of songs and men: a model for multiple choice with herding," Quality & Quantity: International Journal of Methodology, Springer, vol. 41(4), pages 557-568, August.
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    Cited by:

    1. Bask, Miia & Bask, Mikael, 2014. "Social influence and the Matthew mechanism: The case of an artificial cultural market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 412(C), pages 113-119.
    2. Rui Chen & Hai Jiang, 2020. "Assortment optimization with position effects under the nested logit model," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(1), pages 21-33, February.
    3. Jerker Denrell & Gaël Le Mens, 2017. "Information Sampling, Belief Synchronization, and Collective Illusions," Management Science, INFORMS, vol. 63(2), pages 528-547, February.
    4. Berbeglia, Franco & Berbeglia, Gerardo & Van Hentenryck, Pascal, 2021. "Market segmentation in online platforms," European Journal of Operational Research, Elsevier, vol. 295(3), pages 1025-1041.
    5. Andrés Abeliuk & Gerardo Berbeglia & Manuel Cebrian & Pascal Van Hentenryck, 2016. "Assortment optimization under a multinomial logit model with position bias and social influence," 4OR, Springer, vol. 14(1), pages 57-75, March.
    6. Morgan R Frank & Manuel Cebrian & Galen Pickard & Iyad Rahwan, 2017. "Validating Bayesian truth serum in large-scale online human experiments," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-13, May.
    7. Xi, Ning & Zhang, Zi-Ke & Zhang, Yi-Cheng & Ge, Zehui & She, Li & Zhang, Kui, 2014. "Cultural evolution: The case of babies’ first names," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 139-144.

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