Modeling opinion polarization under perception bias

TL;DR

This paper introduces a threshold model to study how perception bias influences opinion polarization in social networks, revealing critical factors and phase transitions that lead to echo chambers and polarization.

Contribution

It presents a novel threshold model incorporating perception bias to analyze opinion dynamics and polarization, highlighting the impact of stubbornness and network structure.

Findings

Polarization occurs when stubbornness exceeds a critical threshold.

Phase transitions between consensus and polarization depend on initial opinions.

Regular networks exhibit step-wise polarization patterns linked to echo chambers.

Abstract

Social networks have provided a platform for the effective exchange of ideas or opinions but also served as a hotbed of polarization. While much research attempts to explore different causes of opinion polarization, the effect of perception bias caused by the network structure itself is largely understudied. To this end, we propose a threshold model that simulates the evolution of opinions by taking into account the perception bias, which is the gap between global information and locally available information from the neighborhood within networks. Our findings suggest that polarization occurs when the collective stubbornness of the population exceeds a critical value which is largely affected by the perception bias. In addition, as the level of stubbornness grows, the occurrence of first-order and second-order phase transitions between consensus and polarization becomes more prevalent, and the types of these phase transitions rely on the initial proportion of active opinions. Notably, for regular network structures, a step-wise pattern emerges that corresponds to various levels of polarization and is strongly associated with the formation of echo chambers. Our research presents a valuable framework for investigating the connection between perception bias and opinion polarization and provides valuable insights for mitigating polarization in the context of biased information.