Social Learning Under Platform Influence: Extreme Consensus and Persistent Disagreement

Abstract

Individuals increasingly rely on social networking platforms to find information and form opinions. However, there are concerns on whether or how these platforms lead to extremism and polarization, especially since they typically aim to maximize engagement which may not align with other social objectives. In this work, we introduce an opinion dynamics model where agents are connected in a social network, and repeatedly update their opinions based on the content shown to them by the platform's personalized recommendation and their neighbors' opinions. We prove that agents always converge to some limiting opinion, which can be categorized into two groups: extreme consensus where all agents agree on an extreme opinion, and persistent disagreement where agents disagree. Extreme consensus is more likely when the platform's influence is weak and connections between agents with differing opinions are strong. The platform increases the extremism of opinions when its influence is either weak or strong, but for different reasons: agents reach an extreme consensus in the former, while agents disagree with opposing extreme opinions in the latter. In contrast, an intermediate level of the platform's influence yields less extreme opinions relative to the other two cases. Lastly, more balanced and less polarized initial opinions are more likely to lead to persistent disagreement rather than extreme consensus.

Keywords: social learning, recommender systems, extreme consensus, persistent disagreement

JEL Classification: D83, D85