Opinion dynamics via search engines (and other algorithmic gatekeepers)

• 成果类型: Article
• 署名作者: Germano, Fabrizio; Sobbrio, Francesco
• 署名单位: Pompeu Fabra University; Luiss Guido Carli University; Leibniz Association; Ifo Institut
• 刊物名称: JOURNAL OF PUBLIC ECONOMICS
• ISSN/ISSBN: 0047-2727
• DOI: 10.1016/j.jpubeco.2020.104188
• 发表日期: 2020
• 关键词: Ranking algorithm; information aggregation; asymptotic learning; Popularity ranking; Personalized ranking; misinformation; fake news

摘要

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. (C) 2020 Elsevier B.V. All rights reserved.