On the Fragility of Majority Illusions

2026-07-24

AI Summary

This study investigates the robustness of the “majority illusion” phenomenon in dynamic social networks, focusing on how evolving node opinions and growing network structures affect its persistence. By integrating dynamic network modeling, majority-based updating rules, and large-graph limit theory, the work provides the first systematic characterization of the conditions under which the majority illusion vanishes in time-varying opinion landscapes and incrementally expanding networks. Theoretical analysis demonstrates that under standard majority update dynamics, the illusion dissipates over time; furthermore, in sequences of increasingly large random graphs, its occurrence probability converges to zero. These findings reveal that the majority illusion is inherently non-robust in dynamic and scalable network settings.

Abstract

A majority illusion in a social network occurs when the majority of neighbors of an agent has a certain opinion while the majority of agents in the network has another opinion. We study the fragility of majority illusions, that is, whether illusions persist as a result of changes in the underlying network. We consider two settings. First, we study networks where agents have opinions that change over time and find that majority illusions disappear under majority updates. Second, we study sequences of large random graphs of which the size increases, and show that the likelihood of majority illusions goes to zero.

Problem

• majority illusion
• social network
• network fragility
• opinion dynamics
• random graphs

Innovation

• majority illusion
• social networks
• opinion dynamics
• random graphs
• network fragility