Impact of the Network Size and Frequency of Information Receipt on Polarization in Social Networks

TL;DR

This paper investigates how network size and the frequency of receiving information influence opinion polarization in social networks, using a dynamical system approach to model and analyze these effects.

Contribution

It introduces a dynamical system model incorporating time intervals between information receipt, revealing how larger networks and higher frequency promote polarization.

Findings

Shorter information receipt intervals increase polarization.

Larger social networks elevate the likelihood of polarization.

A Polarization number predicts critical thresholds for individual polarization.

Abstract

Opinion Dynamics is an interdisciplinary area of research. Psychology and Sociology have proposed models of how individuals form opinions and how social interactions influence this process. Socio-Physicists have interpreted patterns in opinion formation as arising from non-linearity in the underlying process, shaping the models. Agent-based modeling has offered a platform to study the Opinion Dynamics of large groups. This paper recasts recent models in opinion formation into a proper dynamical system, injecting the idea of clock time into evolving opinions. The time interval between successive receipts of new information (frequency of information receipts) becomes a factor to study. Social media has shrunk time intervals between information receipts, increasing their frequency. The recast models show that shorter intervals and larger networks increase an individual's propensity for polarization, defined as an inability to hold a neutral opinion. A Polarization number based on sociological parameters is proposed, with critical values beyond which individuals are prone to polarization, depending on psychological parameters. Reduced time intervals and larger interacting groups can push the Polarization number to critical values, contributing to polarization. The Extent of Polarization is defined as the width of the region around neutral within which an individual cannot hold an opinion. Results are reported for model parameters found in the literature. The findings offer an opportunity to adjust model parameters to align with empirical evidence, aiding the study of Opinion Dynamics in large social networks using Agent-Based Modeling.