Crowding out the truth? : a simple model of misinformation, polarization, and meaningful social interactions

Fabrizio Germano, Vicenç Gómez, Francesco Sobbrio

This paper provides a simple theoretical framework to evaluate the effect of key parameters of ranking algorithms, namely popularity and personalization parameters, on measures of platform engagement, misinformation and polarization. The results show that an increase in the weight assigned to online social interactions (e.g., likes and shares) and to personalized content may increase engagement on the social media platform, while at the same time increasing misinformation and/or polarization. By exploiting Facebook's 2018 "Meaningful Social Interactions" algorithmic ranking update, we also provide direct empirical support for some of the main predictions of the model.

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Year of publication:	October 2022 ; This version: November 2022
Authors:	Germano, Fabrizio ; Gómez, Vicenç ; Sobbrio, Francesco
Publisher:	Munich, Germany : CESifo, Center for Economic Studies & Ifo Institute
Subject:	algorithmic gatekeeper \| ranking algorithms \| popularity ranking \| personalized ranking \| meaningful social interactions \| engagement \| polarization \| misinformation \| Soziale Beziehungen \| Social relations \| Theorie \| Theory \| Ranking-Verfahren \| Ranking method \| Verdrängungseffekt \| Crowding out \| Algorithmus \| Algorithm