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Opinion Dynamics via Search Engines (and other Algorithmic Gatekeepers)

Fabrizio Germano () and Francesco Sobbrio ()

No 6541, CESifo Working Paper Series from CESifo Group Munich

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 that uses an algorithm based on popularity and on personalization. We find that 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 explains the diffusion of misinformation, as websites reporting incorrect information may attract an amplified amount of traffic precisely because they are few. Furthermore, when individuals provide sufficiently positive feedback to the ranking algorithm, popularity-based rankings tend to aggregate information while personalization acts in the opposite direction.

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)
New Economics Papers: this item is included in nep-ict, nep-mic and nep-pol
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

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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
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