Filter Bubble effect in the multistate voter model

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.

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Cited by in corpus (5)

• The effect of Collaborative-Filtering based Recommendation Algorithms on Opinion Polarization
• Analytical and numerical treatment of continuous ageing in the voter model
• Local and global ordering dynamics in multi-state voter models
• Voter model can accurately predict individual opinions in online populations
• Analysis of a voter model with an evolving number of opinion states