Ranking for Engagement: How Social Media Algorithms Fuel Misinformation and Polarization

32 Pages Posted: 17 Oct 2022 Last revised: 5 Jun 2024

See all articles by Fabrizio Germano

Fabrizio Germano

Universitat Pompeu Fabra - Department of Economics and Business

Vicenç Gómez

Universitat Pompeu Fabra - Department of Information and Communication Technologies

Francesco Sobbrio

University of Rome Tor Vergata - Department of Economics and Finance; CESifo (Center for Economic Studies and Ifo Institute)

Multiple version iconThere are 2 versions of this paper

Date Written: October 1, 2022

Abstract

Social media are at the center of countless debates on polarization, misinformation, and even the state of democracy in various parts of the world. An essential feature of social media is their recommendation algorithm that determines the ranking of contents presented to the users. This paper studies the dynamic feedback between a recommender algorithm and user behavior; and develops a theoretical framework to evaluate the effect of popularity parameters on measures of platform and user welfare. The model shows the presence of a fundamental trade-off between platform engagement and user welfare. A higher weight assigned by the algorithm to online social interactions such as likes and shares increases engagement while having a detrimental effect in terms of misinformation-crowding-out the truth-and polarization. Besides increasing actual polarization, an increase in the weight assigned to social interactions may also increase perceived polarization, as it makes it more likely for individuals to see more extreme content-both like-minded and not-in higher-ranked positions. Finally, we provide empirical evidence in support of the main predictions of our model. By leveraging a rich survey dataset from Italy and exploiting Facebook's 2018 "Meaningful Social Interactions" update-which significantly boosted the weight given to social interaction in its ranking algorithm-we find an increase in political polarization and ideological extremism in Italy, following the change in Facebook's algorithm.

Keywords: Social media, recommendation algorithm, ranking algorithm, feedback loop, engagement, misinformation, polarization, popularity ranking, algorithmic gatekeeper

JEL Classification: D72, D83, L82, L86

Suggested Citation

Germano, Fabrizio and Gómez, Vicenç and Sobbrio, Francesco, Ranking for Engagement: How Social Media Algorithms Fuel Misinformation and Polarization (October 1, 2022). Available at SSRN: https://ssrn.com/abstract=4238756 or http://dx.doi.org/10.2139/ssrn.4238756

Fabrizio Germano

Universitat Pompeu Fabra - Department of Economics and Business ( email )

Ramon Trias Fargas 25-27
Barcelona, 08005
Spain
+34-93-542-2729 (Phone)
+34-93-542-1746 (Fax)

Vicenç Gómez

Universitat Pompeu Fabra - Department of Information and Communication Technologies ( email )

Francesco Sobbrio (Contact Author)

University of Rome Tor Vergata - Department of Economics and Finance ( email )

Via columbia 2
Rome, Rome 00123
Italy

CESifo (Center for Economic Studies and Ifo Institute) ( email )

Poschinger Str. 5
Munich, DE-81679
Germany

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