Opinion Dynamics via Search Engines (and other Algorithmic Gatekeepers)

Francesco Sobbrio and Fabrizio Germano

No 962, Working Papers from Barcelona School of Economics

Abstract: Ranking algorithms are the information gatekeepers of the Internet era. We develop a stylized model to study the effects of ranking algorithms on opinion dynamics. We consider a search engine using an algorithm that depends on popularity and on personalization. Popularity-based rankings generate an advantage of the fewer effect: fewer websites reporting a given signal attract more traffic overall. This provides a rationale for the diffusion of misinformation, as traffic to websites reporting incorrect information can be large precisely when there are few of them. Finally, we study conditions under which popularity-based rankings and personalized rankings contribute to asymptotic learning.

Keywords: polarization; search engines; ranking algorithm; search behavior; opinion dynamics; information aggregation; asymptotic learning; misinformation; website traffic; fake news (search for similar items in EconPapers)
JEL-codes: D83 L86 (search for similar items in EconPapers)
Date: 2017-04
New Economics Papers: this item is included in nep-ict, nep-mic and nep-pol
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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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) (2018)
Working Paper: Opinion Dynamics via Search Engines (and other Algorithmic Gatekeepers) (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:bge:wpaper:962

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