Opinion Dynamics via Search Engines (and Other Algorithmic Gatekeepers)
58 Pages Posted: 30 Dec 2016 Last revised: 14 Mar 2020
Date Written: February 1, 2020
Ranking algorithms are the information gatekeepers of the Internet era. We develop a stylized model to study the interplay between a ranking algorithm and individual clicking behavior. We consider a search engine that uses an algorithm based on popularity and on personalization. The analysis shows the presence of a feedback effect, whereby individuals clicking on websites indirectly provide information about their private signals to successive searchers through the popularity-ranking algorithm. Accordingly, when individuals provide sufficiently positive feedback to the ranking algorithm, popularity-based rankings tend to aggregate information while personalization acts in the opposite direction. Moreover, we find that, under fairly general conditions, 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 can potentially explain the diffusion of misinformation, as websites reporting incorrect information may attract an amplified amount of traffic precisely because they are few.
Keywords: Ranking Algorithm, Information Aggregation, Asymptotic Learning, Popularity Ranking, Personalized Ranking, Misinformation, Fake News
JEL Classification: D83
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