Germano, FabrizioSobbrio, Francesco2025-01-272025-01-272020Germano F, Sobbrio F. Opinion dynamics via search engines (and other algorithmic gatekeepers). J Public Econ. 2020 Jul;187:104188. DOI: 10.1016/j.jpubeco.2020.1041880047-2727http://hdl.handle.net/10230/69297Includes supplementary materials for the online appendix.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.application/pdfeng© Elsevier http://dx.doi.org/10.1016/j.jpubeco.2020.104188.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.jpubeco.2020.104188Ranking algorithmInformation aggregationAsymptotic learningPopularity rankingPersonalized rankingMisinformationFake newsinfo:eu-repo/semantics/openAccess