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Opinion dynamics via search engines (and other algorithmic gatekeepers)

Fabrizio Germano and Francesco Sobbrio ()

Journal of Public Economics, 2020, vol. 187, issue C

Abstract: Ranking algorithms are the information gatekeepers of the Internet era. We develop a stylized model to study the interplay between a ranking algorithm and individual clicking behavior. We consider a search engine that uses an algorithm based on popularity and on personalization. The analysis shows the presence of a feedback effect, whereby individuals clicking on websites indirectly provide information about their private signals to successive searchers through the popularity-ranking algorithm. Accordingly, when individuals provide sufficiently positive feedback to the ranking algorithm, popularity-based rankings tend to aggregate information while personalization acts in the opposite direction. Moreover, we find that, under fairly general conditions, popularity-based rankings generate an advantage of the fewer effect: fewer websites reporting a given signal attract relatively more traffic overall. This highlights a novel, ranking-driven channel that can potentially explain the diffusion of misinformation, as websites reporting incorrect information may attract an amplified amount of traffic precisely because they are few.

Keywords: Ranking algorithm; Information aggregation; Asymptotic learning; Popularity ranking; Personalized ranking; Misinformation; Fake news (search for similar items in EconPapers)
JEL-codes: D83 (search for similar items in EconPapers)
Date: 2020
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Related works:
Working Paper: Opinion Dynamics via Search Engines (and other Algorithmic Gatekeepers) (2018) Downloads
Working Paper: Opinion dynamics via search engines (and other algorithmic gatekeepers) (2018) Downloads
Working Paper: Opinion Dynamics via Search Engines (and other Algorithmic Gatekeepers) (2017) Downloads
Working Paper: Opinion Dynamics via Search Engines (and other Algorithmic Gatekeepers) (2017) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:eee:pubeco:v:187:y:2020:i:c:s0047272720300529

DOI: 10.1016/j.jpubeco.2020.104188

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