Quantifying and Minimizing Perception Gap in Social Networks

TL;DR

This paper introduces a new graph-based measure called the perception gap index to quantify perception distortions in social networks, analyzes factors affecting its magnitude, and proposes heuristic methods to minimize it.

Contribution

It presents the perception gap index, analyzes network structures affecting perception distortion, and develops heuristic algorithms for link recommendation to reduce the gap.

Findings

Higher connectivity increases network resilience to perception distortion.

Pronounced community structure increases vulnerability to perception gap.

Heuristic methods effectively reduce perception gap in real-world networks.

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.