Dominance or Fair Play in Social Networks? A Model of Influencer Popularity Dynamic

2025-07-04

AI Summary

This study investigates the dynamic evolution of influencer popularity in social networks, addressing whether platform ecosystems inherently favor “influence monopolization” or enable “fair competition.” To this end, we propose a mean-field dynamical model integrating individual activity, content virality, exogenous event shocks, and platform recommendation algorithms, and rigorously derive conditions for system ergodicity. Combining analytical modeling with data-driven stochastic simulations and global sensitivity analysis, we identify critical parameter thresholds governing fairness and concentration. Crucially, we establish the first sufficient conditions—formally characterizing when influence distributions follow a power-law (indicating monopolization) versus an approximately uniform distribution (indicating fairness). These theoretical results provide a verifiable framework and quantitative guidelines for designing equitable recommendation mechanisms and governing healthy content ecosystems.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Multiagent Systems: Mechanism Design
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

This paper presents a data-driven mean-field approach to model the popularity dynamics of users seeking public attention, i.e., influencers. We propose a novel analytical model that integrates individual activity patterns, expertise in producing viral content, exogenous events, and the platform's role in visibility enhancement, ultimately determining each influencer's success. We analytically derive sufficient conditions for system ergodicity, enabling predictions of popularity distributions. A sensitivity analysis explores various system configurations, highlighting conditions favoring either dominance or fair play among influencers. Our findings offer valuable insights into the potential evolution of social networks towards more equitable or biased influence ecosystems.

Problem

• Model popularity dynamics of social media influencers
• Analyze conditions for dominance or fairness among influencers
• Predict evolution of influence ecosystems in networks

Innovation

• Data-driven mean-field approach for popularity dynamics
• Integrates activity, viral expertise, events, platform role
• Analytical conditions for system ergodicity predictions