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Opinion dynamics via search engines (and other algorithmic gatekeepers)

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Abstract

Ranking algorithms are the information gatekeepers of the Internet era. We develop a stylized framework to study the effects of ranking algorithms on opinion dynamics. We consider rankings that depend on popularity and on personalization. We find that popularity driven rankings can enhance asymptotic learning while personalized ones can both inhibit or enhance it, depending on whether individuals have common or private value preferences. We also find that ranking algorithms can contribute towards the diffusion of misinformation (e.g., “fake news”), since lower ex-ante accuracy of content of minority websites can actually increase their overall traffic share.

Suggested Citation

  • Fabrizio Germano & Francesco Sobbrio, 2016. "Opinion dynamics via search engines (and other algorithmic gatekeepers)," Economics Working Papers 1552, Department of Economics and Business, Universitat Pompeu Fabra, revised Mar 2018.
  • Handle: RePEc:upf:upfgen:1552
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    References listed on IDEAS

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    Cited by:

    1. Ascensión Andina-Díaz & José A. García-Martínez & Antonio Parravano, 2017. "The market for scoops: A dynamic approach," Working Papers 2017-03, Universidad de Málaga, Department of Economic Theory, Málaga Economic Theory Research Center.

    More about this item

    Keywords

    Ranking Algorithms; Opinion Dynamics; Website Trac; Asymptotic Learning; Stochastic Choice; Misinformation; Polarization; Search Engines; Fake News.;

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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