Opinion Dynamics: A Comprehensive Overview

2025-11-01

AI Summary

Opinion dynamics research has long suffered from disciplinary fragmentation and the absence of a unifying theoretical framework across computer science, physics, and social science. Method: We propose the first interdisciplinary analytical framework integrating these fields, systematically classifying three core mechanisms—consensus formation, propagation efficiency, and individual heterogeneity—and modeling opinion evolution using mathematical analysis, graph theory, and complex systems theory to characterize convergence properties, polarization pathways, and the influence of agent attributes (e.g., stubbornness, activity). We further design a dynamic control strategy that balances theoretical rigor with algorithmic implementability. Contribution/Results: The framework advances cross-disciplinary integration and demonstrates practical efficacy in viral marketing: it enables accurate identification of critical nodes and achieves influence maximization, while quantitatively assessing how user-specific features modulate opinion trajectory dynamics.

Technology Category

• Multiagent Systems: Mechanism Design
• Application Domains: Humanities & Computational Social Science
• Game Theory and Economic Paradigms: Social Choice / Voting

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Unifying fragmented opinion dynamics models across disciplines
• Analyzing convergence properties and polarization in social networks
• Exploring how user characteristics influence opinion diffusion outcomes

Innovation

• Unified framework categorizes opinion dynamics models
• Analyzes convergence properties and viral marketing algorithms
• Explores node characteristics shaping diffusion outcomes