Emergence of echo chambers in a noisy adaptive voter model

TL;DR

This paper investigates how noisy adaptive voter models can lead to the emergence of echo chambers, showing a phase transition influenced by noise and blocking behavior through simulations and mean-field theory.

Contribution

It introduces a model combining noise and belief perseverance to explain the formation of echo chambers in social networks, supported by simulations and theoretical analysis.

Findings

Transition between single community and multiple echo chambers observed.

Echo chambers form more readily with increased blocking and noise.

Mean-field theory accurately predicts phase transition points.

Abstract

Belief perseverance is the widely documented tendency of holding to a belief, even in the presence of contradicting evidence. In online environments, this tendency leads to heated arguments with users ``blocking'' each other. Introducing this element to opinion modelling in a social network, leads to an adaptive network where agents tend to connect preferentially to like-minded peers. In this work we study how this type of dynamics behaves in the voter model with the addition of a noise that makes agents change opinion at random. As the intensity of the noise and the propensity of users blocking each other is changed, we observe a transition between 2 phases. One in which there is only one community in the whole network and another where communities arise and in each of then there is a very clear majority opinion, mimicking the phenomenon of echo chambers. These results are obtained with simulations and with a mean-field theory.