Opinion fluctuations and disagreement in social networks

TL;DR

This paper introduces a model of opinion dynamics in social networks with stubborn and regular agents, showing persistent fluctuations and disagreement driven by network structure and stubbornness, with implications for large-scale societies.

Contribution

The paper presents a tractable stochastic opinion model that captures long-term disagreement and opinion fluctuations, highlighting the impact of stubborn agents and network structure on belief dynamics.

Findings

Beliefs never fully converge when stubborn agents hold different opinions.

Beliefs fluctuate ergodically, converging in distribution to a non-degenerate random vector.

In large, fluid societies, a homogeneous influence condition emerges, equalizing belief moments among most regular agents.

Abstract

We study a tractable opinion dynamics model that generates long-run disagreements and persistent opinion fluctuations. Our model involves an inhomogeneous stochastic gossip process of continuous opinion dynamics in a society consisting of two types of agents: regular agents, who update their beliefs according to information that they receive from their social neighbors; and stubborn agents, who never update their opinions. When the society contains stubborn agents with different opinions, the belief dynamics never lead to a consensus (among the regular agents). Instead, beliefs in the society fail to converge almost surely, the belief profile keeps on fluctuating in an ergodic fashion, and it converges in law to a non-degenerate random vector. The structure of the network and the location of the stubborn agents within it shape the opinion dynamics. The expected belief vector evolves according to an ordinary differential equation coinciding with the Kolmogorov backward equation of a continuous-time Markov chain with absorbing states corresponding to the stubborn agents and converges to a harmonic vector, with every regular agent's value being the weighted average of its neighbors' values, and boundary conditions corresponding to the stubborn agents'. Expected cross-products of the agents' beliefs allow for a similar characterization in terms of coupled Markov chains on the network. We prove that, in large-scale societies which are highly fluid, meaning that the product of the mixing time of the Markov chain on the graph describing the social network and the relative size of the linkages to stubborn agents vanishes as the population size grows large, a condition of \emph{homogeneous influence} emerges, whereby the stationary beliefs' marginal distributions of most of the regular agents have approximately equal first and second moments.