Quantifying and Minimizing Perception Gap in Social Networks

2025-11-15

AI Summary

This study addresses structure-induced perception bias in social networks—systematic deviations between individuals’ local perceptions and the global opinion distribution (e.g., majority illusion, echo chambers). Method: We propose the first *Perception Gap Index* for continuous opinion spaces, grounded in spectral graph theory to quantify such bias; analyze how network connectivity and community structure mechanistically distort perception; prove that link recommendation to minimize perception gap is APX-hard—and thus not polynomial-time approximable. Contribution/Results: We design multiple efficient heuristic algorithms and validate them on real-world social network datasets. Under constrained budget, our methods significantly reduce perception gap, closely approaching optimal performance and thereby enhancing users’ cognitive accuracy.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Distributed Search
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Sentiment analysis and opinion mining

Abstract

Social media has transformed global communication, yet its network structure can systematically distort perceptions through effects like the majority illusion and echo chambers. We introduce the perception gap index, a graph-based measure that quantifies local-global opinion divergence, which can be viewed as a generalization of the majority illusion to continuous settings. Using techniques from spectral graph theory, we demonstrate that higher connectivity makes networks more resilient to perception distortion. Our analysis of stochastic block models, however, shows that pronounced community structure increases vulnerability. We also study the problem of minimizing the perception gap via link recommendation with a fixed budget. We prove that this problem does not admit a polynomial-time algorithm for any bounded approximation ratio, unless P = NP. However, we propose a collection of efficient heuristic methods that have been demonstrated to produce near-optimal solutions on real-world network data.

Problem

• Quantifying opinion divergence between local and global network views
• Analyzing how network structure affects perception distortion vulnerability
• Developing methods to minimize perception gaps through link recommendations

Innovation

• Introducing perception gap index to quantify opinion divergence
• Using spectral graph theory to analyze network connectivity effects
• Proposing efficient heuristic methods for minimizing perception gap