Opinion Dynamics in Two-Step Process: Message Sources, Opinion Leaders and Normal Agents

TL;DR

This paper introduces the Two-Step Model to better understand opinion formation in social networks by explicitly modeling message sources, opinion leaders, and normal agents, and analyzes how various factors influence steady-state opinions.

Contribution

It presents a unified two-step framework that captures the influence of opinion leaders on normal agents, improving upon single-network models and validated through numerical and social experiments.

Findings

Opinion leaders significantly influence normal agents' opinions.

Factors like message distribution and stubbornness affect opinion variance.

The model outperforms existing models in accuracy.

Abstract

According to mass media theory, the dissemination of messages and the evolution of opinions in social networks follow a two-step process. First, opinion leaders receive the message from the message sources, and then they transmit their opinions to normal agents. However, most opinion models only consider the evolution of opinions within a single network, which fails to capture the two-step process accurately. To address this limitation, we propose a unified framework called the Two-Step Model, which analyzes the communication process among message sources, opinion leaders, and normal agents. In this study, we examine the steady-state opinions and stability of the Two-Step Model. Our findings reveal that several factors, such as message distribution, initial opinion, level of stubbornness, and preference coefficient, influence the sample mean and variance of steady-state opinions. Notably, normal agents' opinions tend to be influenced by opinion leaders in the two-step process. We also conduct numerical and social experiments to validate the accuracy of the Two-Step Model, which outperforms other models on average. Our results provide valuable insights into the factors that shape social opinions and can guide the development of effective strategies for opinion guidance in social networks.