Advanced opinion dynamics modeling is vital for deciphering social behavior, emphasizing its role in mitigating polarization and securing cyberspace. To synergize mechanistic interpretability with data-driven flexibility, recent studies have explored the integration of Physics-Informed Neural Networks (PINNs) for opinion modeling. Despite this promise, existing methods are tailored to incomplete priors, lacking a comprehensive physical system to integrate dynamics from local, global, and endogenous levels. Moreover, penalty-based constraints adopted in existing methods struggle to deeply encode physical priors, leading to optimization pathologies and discrepancy between latent representations and physical transparency. To this end, we offer a physical view to interpret opinion dynamics via Diffusion-Convection-Reaction (DCR) system inspired by interacting particle theory. Building upon the Neural ODEs, we define the neural opinion dynamics to coordinate neural networks with physical priors, and further present the OPINN, a physics-informed neural framework for opinion dynamics modeling. Evaluated on real-world and synthetic datasets, OPINN achieves state-of-the-art performance in opinion evolution forecasting, offering a promising paradigm for the nexus of cyber, physical, and social systems.

翻译：先进的观点动力学建模对于解读社会行为至关重要，其作用在于缓解两极分化和保障网络空间安全。为了将机制可解释性与数据驱动的灵活性相结合，近期研究探索了将物理信息神经网络（PINNs）应用于观点建模。尽管前景广阔，现有方法仅针对不完整的先验知识进行定制，缺乏一个能够整合局部、全局和内生层面动力学的综合物理系统。此外，现有方法采用的基于惩罚的约束难以深度编码物理先验，导致优化病态以及潜在表征与物理透明度之间的差异。为此，我们提出一种基于相互作用粒子理论启发的扩散-对流-反应（DCR）系统来解释观点动力学的物理视角。在神经常微分方程的基础上，我们定义了神经观点动力学以协调神经网络与物理先验，并进一步提出了OPINN——一个用于观点动力学建模的物理信息神经框架。在真实世界和合成数据集上的评估表明，OPINN在观点演化预测方面实现了最先进的性能，为网络、物理和社会系统的交叉领域提供了一个有前景的范式。