From mean-field to complex topologies: network effects on the algorithmic bias model

TL;DR

This paper investigates how different social network structures influence opinion formation and polarization, especially considering algorithmic bias, using a recent opinion dynamic model across various network topologies.

Contribution

It compares the effects of mean-field, scale-free, random, and benchmark networks on opinion dynamics with algorithmic bias, highlighting the role of topology in polarization.

Findings

Network topology significantly affects opinion fragmentation.

Scale-free networks tend to foster higher polarization.

Convergence times vary with network structure.

Abstract

Nowadays, we live in a society where people often form their opinion by accessing and discussing contents shared on social networking websites. While these platforms have fostered information access and diffusion, they represent optimal environments for the proliferation of polluted contents, which is argued to be one of the co-causes of polarization/radicalization. Moreover, recommendation algorithms - intended to enhance platform usage - are likely to augment such phenomena, generating the so called Algorithmic Bias. In this work, we study the impact that different network topologies have on the formation and evolution of opinion in the context of a recent opinion dynamic model which includes bounded confidence and algorithmic bias. Mean-field, scale-free and random topologies, as well as networks generated by the Lancichinetti-Fortunato-Radicchi benchmark, are compared in terms of opinion fragmentation/polarization and time to convergence.