Popularity dynamics in social networks show persistent channel dominance
Ergodic and Metastable Behavior of Stochastic Popularity Dynamics in Social Networks
Social and Information Networks
Summary
This paper looks at why certain channels or content creators get more popular than others on social networks. The authors created a mathematical model showing that if content quality is ignored, one channel eventually dominates completely. However, when all have some quality, popularity keeps shifting but often sticks around one leader for a long time before changing. They also explored how platforms might reduce unfair advantages caused by network effects. Their model matched patterns seen in YouTube data.
What this means in practice
- •For social media platform designers: Design controls to reduce biased exposure and prevent one channel from dominating unjustly due to network effects and popularity feedback.
- •For digital marketing teams: Predict and interpret how small differences in content quality affect long-term popularity trends affected by social reinforcement.
Tested on one dataset.
Authors
Chiara Ravazzi, Paolo Frasca, Gaya Cocca
Abstract
This paper studies popularity dynamics in social networks, where users' engagement with competing channels is driven by intrinsic content quality, social influence, and popularity feedback. We introduce a stochastic model in which users interact through a directed influence network and channel exposure depends jointly on these three mechanisms. We first characterize the resulting dynamics in the absence of intrinsic quality, showing that popularity concentrates asymptotically on a single channel, leading to a winner-takes-all regime. When all channels have positive intrinsic quality, we establish ergodicity of the popularity process and characterize its long-run behavior. We further show that, when intrinsic quality is small, the system exhibits metastable behavior: it can remain for long periods near configurations dominated by a single channel before transitioning to another. These results highlight how weak quality differences can coexist with strong path dependence induced by popularity feedback. We then investigate platform-level interventions and propose controls that compensate for network-induced exposure biases and mitigate excessive concentration. Analytical results are illustrated through numerical simulations and an empirical analysis of YouTube data, whose observed popularity dynamics are consistent with the proposed stochastic framework. The results provide a mathematical framework for understanding the interplay between quality, social influence, and reinforcement in online popularity dynamics.