Echoes of Disagreement: Measuring Disparity in Social Consensus

2025-04-10

AI Summary

This paper addresses the quantification of influence disparity among social groups during consensus formation. Building upon the DeGroot and Friedkin-Johnsen opinion dynamics models, we formally define “group influence disparity” and design a provably optimal polynomial-time algorithm to minimize it. Theoretically, we fully characterize the class of graph structures admitting exact optimal solutions and precisely analyze how graph interventions—such as node contraction and edge addition—affect disparity. Methodologically, our approach integrates opinion dynamics modeling, graph-theoretic optimization, and convex optimization analysis. Empirical evaluation across multiple real-world social network datasets demonstrates the algorithm’s effectiveness and controllability in adjusting disparity. Our framework provides a theoretically grounded and practically implementable tool for opinion steering and fairness-aware interventions in socio-technical systems.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Game Theory and Economic Paradigms: Social Choice / Voting

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Public discourse and opinions stem from multiple social groups. Each group has beliefs about a topic (such as vaccination, abortion, gay marriage, etc.), and opinions are exchanged and blended to produce consensus. A particular measure of interest corresponds to measuring the influence of each group on the consensus and the disparity between groups on the extent to which they influence the consensus. In this paper, we study and give provable algorithms for optimizing the disparity under the DeGroot or the Friedkin-Johnsen models of opinion dynamics. Our findings provide simple poly-time algorithms to optimize disparity for most cases, fully characterize the instances that optimize disparity, and show how simple interventions such as contracting vertices or adding links affect disparity. Finally, we test our developed algorithms in a variety of real-world datasets.

Problem

• Measure influence disparity among social groups in consensus formation
• Develop algorithms to optimize disparity in opinion dynamics models
• Analyze impact of network changes on consensus disparity

Innovation

• Optimizing disparity using DeGroot model
• Poly-time algorithms for disparity optimization
• Testing algorithms on real-world datasets