Filter Bubble effect in the multistate voter model

TL;DR

This paper models how personalized content recommendations on social media can lead to persistent opinion polarization, showing a phase transition from consensus to polarization depending on the influence strength.

Contribution

It introduces a multistate voter model incorporating personalized recommendations and demonstrates a phase transition between consensus and polarization regimes.

Findings

Existence of a critical threshold for opinion polarization

Consensus becomes impossible as system size grows large

Personalized recommendations can induce persistent opinion clusters

Abstract

Social media influence online activity by recommending to users content strongly correlated with what they have preferred in the past. In this way they constrain users within filter bubbles that strongly limit their exposure to new or alternative content. We investigate this type of dynamics by considering a multistate voter model where, with a given probability $\lambda$, a user interacts with a "personalized information" suggesting the opinion most frequently held in the past. By means of theoretical arguments and numerical simulations, we show the existence of a nontrivial transition between a region (for small $\lambda$) where consensus is reached and a region (above a threshold $\lambda_c$) where the system gets polarized and clusters of users with different opinions persist indefinitely. The threshold always vanishes for large system size $N$, showing that consensus becomes impossible for a large number of users. This finding opens new questions about the side effects of the widespread use of personalized recommendation algorithms.