Social network cascades influence how opinions spread and change
From Content Sharing to Opinion Shaping: A Theoretical Framework for Cascade-Driven Opinion Dynamics
Social and Information Networks
Summary
People often share information and form opinions online in connected ways, but most studies look at these separately. This paper builds on a model that treats sharing and opinion changes as connected processes. The authors developed new mathematical tools to better understand these linked dynamics and showed that opinions can remain diverse or become polarized instead of everyone agreeing. Their work helps explain how information sharing in social networks shapes public opinion over time.
What this means in practice
- •For social media platform designers: Use the model to predict how user interactions can lead to opinion polarization or consensus in networks with known community structures.
- •For marketing analytics teams: Incorporate cascade-driven opinion updates to better forecast the impact of information spread on collective customer attitudes.
Tested on simulated data.
Authors
Elisabetta Biondi, Chiara Boldrini, Andrea Passarella, Marco Conti
Abstract
Online social networks (OSNs) tightly couple information diffusion and opinion formation, yet most existing models treat these processes separately. Building on the Friedkin-Johnsen on Cascades (FJC) model, which integrates opinion updating within stochastic information cascades, we advance its analytical characterization and systematic validation. We derive an exact conditional layer-wise recursion that reveals the statistical coupling between diffusion realizations and opinion evolution, showing that a closed-form recursion in unconditional expectations is not available. To overcome this limitation, we introduce a mean-field decoupling approximation (MFA) that yields a tractable linear recursion for the expected opinion trajectory. We also develop scalable tree-based reductions that drastically reduce computational complexity while preserving structural diffusion properties, and validate the analytical recursion and its approximations through extensive simulations. Using Stochastic Block Model networks with controlled initial opinion configurations, we show that, unlike the classical Friedkin-Johnsen model, which typically leads to global homogenization, the FJC dynamics generate richer regimes of bounded assimilation and sustained polarization. Overall, this work bridges diffusion and influence modeling, providing a theoretical foundation for understanding how cascade-driven interactions reshape collective opinion landscapes in structured social networks.