Opinion formation on social networks with algorithmic bias: Dynamics and bias imbalance

TL;DR

This paper models how algorithmic bias in social networks influences opinion dynamics, revealing complex phase behaviors and convergence properties affected by bias strength, community structure, and noise.

Contribution

It introduces a mathematical framework for opinion evolution under algorithmic bias, accounting for network structure, noise, and group interactions, and analyzes phase transitions and convergence times.

Findings

Strong bias can determine the final opinion state.

The model exhibits phases of coexistence, consensus, and polarization.

Convergence times vary widely, with signs of critical slowing down.

Abstract

We investigate opinion dynamics and information spreading on networks under the influence of content filtering technologies. The filtering mechanism, present in many online social platforms, reduces individuals' exposure to disagreeing opinions, producing algorithmic bias. We derive evolution equations for global opinion variables in the presence of algorithmic bias, network community structure, noise (independent behavior of individuals), and pairwise or group interactions. We consider the case where the social platform shows a predilection for one opinion over its opposite, unbalancing the dynamics in favor of that opinion. We show that if the imbalance is strong enough, it may determine the final global opinion and the dynamical behavior of the population. We find a complex phase diagram including phases of coexistence, consensus, and polarization of opinions as possible final states of the model, with phase transitions of different order between them. The fixed point structure of the equations determines the dynamics to a large extent. We focus on the time needed for convergence and conclude that this quantity varies within a wide range, showing occasionally signatures of critical slowing down and meta-stability.