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

TL;DR

This paper presents a simple theoretical model to analyze how ranking algorithms' parameters influence social media engagement, misinformation, and polarization, supported by empirical evidence from Facebook's 2018 update.

Contribution

It introduces a basic theoretical framework linking ranking parameters to engagement, misinformation, and polarization, and empirically validates some predictions using Facebook data.

Findings

Increased weight on social interactions boosts engagement.

Higher personalization can increase misinformation.

Empirical support from Facebook's 2018 ranking update.

Abstract

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.