🤖 AI Summary
This study investigates whether the pronounced inequality in user interactions on online social platforms stems from structural mechanisms inherent to digital environments. By constructing cross-platform user–post bipartite networks that integrate both posting and interaction behaviors, the authors quantify interaction inequality using Kullback–Leibler divergence, the inverse coefficient of variation, and a log-transformed Gini coefficient. The analysis provides the first systematic evidence that such inequality remains stable over time across diverse platforms, scales, and governance models, indicating it is not a stochastic artifact but rather driven by deep-seated structural constraints. These findings reveal the systemic origins of how visibility and participation are allocated in online spaces, underscoring the role of platform architecture in shaping user engagement disparities.
📝 Abstract
User interactions on social media platforms are unevenly distributed: a small subset of users consistently captures most of the activity, while the majority remains marginal. Although this pattern is well known and often described by power-law distributions, its consistency across time, platforms, and interaction types has not been systematically assessed. In this study, we analyze user-post bipartite networks from multiple social media platforms. We consider both active contributions (posts) and passive engagement (likes and comments), and quantify distributional properties and inequality using a KL-divergence-based model comparison, an inverse coefficient of variation, and a log-transformed Gini index. Our results show that interaction inequality remains stable over time within each platform. This holds across systems with different sizes, topical focuses, and governance models. These findings indicate that inequality in online engagement is not incidental but reflects structural constraints that shape how visibility and participation are distributed in digital environments.