  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.
