Opinion dynamics via search engines (and other algorithmic gatekeepers)

Fabrizio Germano and Francesco Sobbrio

Economics Working Papers from Department of Economics and Business, Universitat Pompeu Fabra

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.

Keywords: Ranking Algorithms; Opinion Dynamics; Website Traffic; Asymptotic Learning; Stochastic Choice; Misinformation; Polarization; Search Engines; Fake News. (search for similar items in EconPapers)
JEL-codes: D83 L86 (search for similar items in EconPapers)
Date: 2016-12, Revised 2018-03
New Economics Papers: this item is included in nep-cmp, nep-mic and nep-pol
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Opinion dynamics via search engines (and other algorithmic gatekeepers) (2020)
Working Paper: Opinion Dynamics via Search Engines (and other Algorithmic Gatekeepers) (2018)
Working Paper: Opinion Dynamics via Search Engines (and other Algorithmic Gatekeepers) (2017)
Working Paper: Opinion Dynamics via Search Engines (and other Algorithmic Gatekeepers) (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:upf:upfgen:1552

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