The interaction between negative emotion contagion and the spread of extreme opinions on social media can lead to serious consequences, highlighting the need to understand the co-evolution of opinion and emotion. However, most existing studies treat opinion and emotion dynamics as independent processes. The few models that explore their interplay often do not fully capture their distinct dynamic mechanisms and tend to overlook the mutual reinforcement between opinion convergence and emotion contagion. To address these limitations, we propose the dynamics of opinion and emotion on social media (DOES) model, which employs two differential equations to represent the separate dynamics of opinion and emotion, and introduces coupling functions that describe their mutual reinforcement. We analyze the model’s steady-state conditions and validate the results through simulations. Prediction experiments on real-world datasets demonstrate the model’s ability to reproduce key patterns. DOES provides a novel framework for modeling the coevolution of opinion and emotion, offering insights into emotional regulation and discourse moderation on social media platforms.

Polarization and fragmentation in social media amplify user biases, making it increasingly important to understand the evolution of opinions. Opinion dynamics provide interpretability for studying opinion evolution, yet incorporating these insights into predictive models remains challenging. This challenge arises due to the inherent complexity of the diversity of opinion fusion rules and the difficulty in capturing equilibrium states while avoiding over-smoothing. This paper constructs a unified opinion dynamics model to integrate different opinion fusion rules and generates corresponding synthetic datasets. To fully leverage the advantages of unified opinion dynamics, we introduces UniGO, a framework for modeling opinion evolution on graphs. Using a coarsen-refine mechanism, UniGO efficiently models opinion dynamics through a graph neural network, mitigating over-smoothing while preserving equilibrium phenomena. UniGO leverages pretraining on synthetic datasets, which enhances its ability to generalize to real-world scenarios, providing a viable paradigm for applications of opinion dynamics. Experimental results on both synthetic and real-world datasets demonstrate UniGO's effectiveness in capturing complex opinion formation processes and predicting future evolution. The pretrained model also shows strong generalization capability, validating the benefits of using synthetic data to boost real-world performance.