A Bounded-Confidence Model of Opinion Dynamics with Heterogeneous Node-Activity Levels

TL;DR

This paper extends the bounded-confidence model of opinion dynamics by incorporating heterogeneous node activity levels, revealing that such heterogeneity leads to longer convergence times and increased opinion fragmentation.

Contribution

It introduces a generalized Deffuant--Weisbuch model with node weights to simulate heterogeneous sociability in social networks, a novel approach in opinion dynamics modeling.

Findings

Heterogeneous node weights increase convergence times.

Node weights lead to more opinion fragmentation.

Heterogeneity affects the speed and nature of opinion consensus.

Abstract

Agent-based models of opinion dynamics allow one to examine the spread of opinions between entities and to study phenomena such as consensus, polarization, and fragmentation. By studying a model of opinion dynamics on a social network, one can explore the effects of network structure on these phenomena. In social networks, some individuals share their ideas and opinions more frequently than others. These disparities can arise from heterogeneous sociabilities, heterogeneous activity levels, different prevalences to share opinions when engaging in a social-media platform, or something else. To examine the impact of such heterogeneities on opinion dynamics, we generalize the Deffuant--Weisbuch (DW) bounded-confidence model (BCM) of opinion dynamics by incorporating node weights. The node weights allow us to model agents with different probabilities of interacting. Using numerical simulations, we systematically investigate (using a variety of network structures and node-weight distributions) the effects of node weights, which we assign uniformly at random to the nodes. We demonstrate that introducing heterogeneous node weights results in longer convergence times and more opinion fragmentation than in a baseline DW model. The node weights in our BCM allow one to consider a variety of sociological scenarios in which agents have heterogeneous probabilities of interacting with other agents.