Opinion Dynamics via Search Engines (and other Algorithmic Gatekeepers)


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.