Opinion Dynamics: A Comprehensive Overview

TL;DR

This survey unifies diverse models of opinion dynamics across disciplines, analyzing their convergence, viral marketing implications, and the influence of user traits to foster interdisciplinary collaboration.

Contribution

It provides a comprehensive, unified framework categorizing opinion dynamics models and synthesizes key findings across multiple fields.

Findings

Analyzes convergence properties and final configurations of opinion models.

Reviews algorithmic and complexity results related to viral marketing.

Explores how user traits influence opinion diffusion outcomes.

Abstract

Opinion dynamics, the evolution of individuals through social interactions, is an important area of research with applications ranging from politics to marketing. Due to its interdisciplinary relevance, studies of opinion dynamics remain fragmented across computer science, mathematics, the social sciences, and physics, and often lack shared frameworks. This survey bridges these gaps by reviewing well-known models of opinion dynamics within a unified framework and categorizing them into distinct classes based on their properties. Furthermore, the key findings on these models are covered in three parts: convergence properties, viral marketing, and user characteristics. We first analyze the final configuration (consensus vs polarized) and convergence time for each model. We then review the main algorithmic, complexity, and combinatorial results in the context of viral marketing. Finally, we explore how node characteristics, such as stubbornness, activeness, or neutrality, shape diffusion outcomes. By unifying terminology, methods, and challenges across disciplines, this paper aims to foster cross-disciplinary collaboration and accelerate progress in understanding and harnessing opinion dynamics.