A dynamical model of platform choice and online segregation

TL;DR

This paper presents a dynamic model of platform choice using multi-agent reinforcement learning to understand online polarization and echo chambers, revealing how user preferences influence societal discourse.

Contribution

It extends Social Feedback Theory with reinforcement learning to model how users select platforms and how this leads to polarization and dominance of certain platforms.

Findings

Online environments can become polarized echo chambers.

A dominant platform can marginalize others into extremity.

Polarization can occur even with a preference for diverse views.

Abstract

In order to truly understand how social media might shape online discourses or contribute to societal polarization, we need refined models of platform choice, that is: models that help us understand why users prefer one social media platform over another. This study develops a dynamic model of platform selection, extending Social Feedback Theory by incorporating multi-agent reinforcement learning to capture how user decisions are shaped by past rewards across different platforms. A key parameter ($\mu$) in the model governs users' tendencies to either seek approval from like-minded peers or engage with opposing views. Our findings reveal that online environments can evolve into suboptimal states characterized by polarized, strongly opinionated echo chambers, even when users prefer diverse perspectives. Interestingly, this polarizing state coexists with another equilibrium, where users gravitate toward a single dominant platform, marginalizing other platforms into extremity. Using agent-based simulations and dynamical systems analysis, our model underscores the complex interplay of user preferences and platform dynamics, offering insights into how digital spaces might be better managed to foster diverse discourse.