How Recommendation Algorithms Shape Social Networks: An Adaptive Voter Model Approach

TL;DR

This paper uses an adaptive voter model to analyze how different social connection mechanisms, especially algorithmic friend recommendations, influence opinion polarization and network fragmentation, highlighting increased echo chambers.

Contribution

It introduces a comparative simulation study of global versus local rewiring mechanisms, revealing how recommendation algorithms can intensify social fragmentation and echo chambers.

Findings

Local rewiring increases polarization and network fragmentation.

Recommendation algorithms lead to more isolated nodes and smaller disconnected components.

Clustered networks with high homophily amplify fragmentation effects.

Abstract

The rise of social media and recommendation algorithms has sparked concerns about their role in fostering opinion polarization and echo chambers. We study these phenomena using an adaptive voter model to compare two connection mechanisms: "free" global rewiring, where individuals connect with anyone sharing their opinion, and "friend-of-a-friend" local rewiring, which mimics algorithmic link recommendations on platforms like Facebook or LinkedIn. Simulations across different network topologies reveal that local rewiring increases final-state polarization of the system and fragments social networks into many disconnected components. The usual phase transition into two disconnected components turns into a fragmentation of smaller components, leading to an increase in echo chambers as well as many isolated nodes. This effect is most pronounced in clustered networks with high homophily in rewiring, illustrating how recommendation algorithms can intensify social fragmentation by changing the very structure of the network.