Modeling of Social Media Opinion Dynamics: Predictions and Interventions

In the era of digital communication, understanding the dynamics of opinion formation and polarization on social media platforms is crucial for both scientific inquiry and societal well-being. This work develops a probabilistic framework to model and simulate opinion dynamics on X (formerly Twitter) using retweet interactions between users and influential accounts (“leaders”). The model introduces a two-stage process—content exposure followed by engagement—parametrized by interpretable variables representing algorithmic influence and user behavior. Empirical data from Italian Twitter (2018–2022) inform model calibration, enabling accurate monthly predictions of user-leader retweet matrices. Analytical insights are drawn through connections to population genetics models, revealing how algorithmic biases and levels of user engagement jointly drive polarization dynamics. The framework further enables evaluation of hypothetical interventions, such as modifying recommendation system parameters, to assess their impact on network diversity and modularity. The results show that modifying certain algorithmic parameters can shift patterns of user attention and network structure, influencing levels of polarization and content diversity. This study provides a flexible modeling framework for exploring the interplay between user behavior and platform design, offering insights into the potential impacts of intervention strategies on social media dynamics.