2026-04-17T19:16:18Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/692972025-12-20T17:01:45Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Germano, Fabrizio author Sobbrio, Francesco author 2025-01-27T12:55:04Z 2025-01-27T12:55:04Z 2020 Includes 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. Germano acknowledges financial support from grant ECO2017-89240-P (AEI/FEDER, UE), from Fundación BBVA (grant “Innovación e Información en la Economía Digital”) and also from the Spanish Ministry of Economy and Competitiveness, through the Severo Ochoa Programme for Centres of Excellence in R&D (SEV-2015-0563). Ranking algorithm Information aggregation Asymptotic learning Popularity ranking Personalized ranking Misinformation Fake news Opinion dynamics via search engines (and other algorithmic gatekeepers)