Opinion Dynamics via Search Engines (and other Algorithmic Gatekeepers)

• Authors: Fabrizio Germano.
• BSE Working Paper: 110436 | April 17
• Keywords: polarization , search engines , ranking algorithm , search behavior , opinion dynamics , information aggregation , asymptotic learning , misinformation , website traffic , fake news
• JEL codes: D83, L86
• polarization
• search engines
• ranking algorithm
• search behavior
• opinion dynamics
• information aggregation
• asymptotic learning
• misinformation
• website traffic
• fake news

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.