Meritocracy versus Matthew-effect: Two underlying network formation mechanisms of online social platforms

2025-10-31

AI Summary

This paper investigates why content-oriented platforms (e.g., TikTok, Bilibili) exhibit significantly more skewed follower distributions than relationship-oriented platforms (e.g., Facebook, LinkedIn). We identify and formalize two fundamental network-generation mechanisms: “meritocracy”—a capability-based recognition process—and the “Matthew effect”—a cumulative-advantage mechanism—each underpinning the distinct evolutionary logics of the two platform types. For the first time, we treat these as decoupled, composable mechanistic units and propose a hybrid generative model integrating both. Through rigorous theoretical analysis and numerical simulations, the model reproduces key empirical properties: scale-free degree distributions, small-world characteristics, and power-law relationships between in-degree and rank. The model achieves high-fidelity fits across multiple real-world platform datasets, revealing structural drivers of power centralization in content ecosystems and providing a mechanistic foundation for creator behavior.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Natural Language Processing: Generation

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

With the rapid development of the internet industry, online social networks have come to play an increasingly significant role in everyday life. In recent years, content-based emerging platforms such as TikTok, Instagram, and Bilibili have diverged fundamentally in their underlying logic from traditional connection-based social platforms like Facebook and LinkedIn. Empirical data on follower counts and follower-count-based rankings reveal that the distribution of social power varies significantly across different types of platforms, with content-based platforms exhibiting notably greater inequality. Here we propose two fundamental network formation mechanisms: a meritocracy-based model and a Matthew-effect-based model, designed to capture the formation logic underlying traditional and emerging social networks, respectively. Through theoretical and numerical analysis, we demonstrate that both models replicate salient statistical features of social networks including scale-free and small-world property, while also closely match empirical patterns on the relationship between in-degrees and in-degree rankings, thereby capturing the distinctive distributions of social power in respective platforms. Moreover, networks such as academic collaboration networks, where the distribution of social power usually lies between that of traditional and emerging platorms, can be interpreted through a hybrid of the two proposed mechanisms. Deconstructing the formation mechanisms of online social networks offers valuable insights into the evolution of the content ecosystems and the behavioral patterns of content creators on online social platforms.

Problem

• Modeling network formation mechanisms in online social platforms
• Explaining inequality in social power distribution across platforms
• Comparing meritocracy-based and Matthew-effect-based network formation models

Innovation

• Proposes meritocracy-based network formation mechanism
• Introduces Matthew-effect-based network formation model
• Combines both mechanisms for hybrid network interpretation