Opinion Dynamics via Search Engines (and other Algorithmic Gatekeepers)
Fabrizio Germano () and
Francesco Sobbrio ()
No 962, Working Papers from Barcelona Graduate School of Economics
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: search engines; ranking algorithm; search behavior; opinion dynamics; information aggregation; asymptotic learning; misinformation; polarization; website traffic; fake news (search for similar items in EconPapers)
JEL-codes: D83 L86 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ict, nep-mic and nep-pol
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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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