Social media platforms have played a key role in weaponizing the polarization of social, political, and democratic processes. This is, mainly, because they are a medium for opinion formation. Opinion dynamic models are a tool for understanding the role of specific social factors on the acceptance/rejection of opinions because they can be used to analyze certain assumptions on human behaviors. This work presents a framework that uses concurrent set relations as the formal basis to specify, simulate, and analyze social interaction systems with dynamic opinion models. Standard models for social learning are obtained as particular instances of the proposed framework. It has been implemented in the Maude system as a fully executable rewrite theory that can be used to better understand how opinions of a system of agents can be shaped. This paper also reports an initial exploration in Maude on the use of reachability analysis, probabilistic simulation, and statistical model checking of important properties related to opinion dynamic models.

翻译：社交媒体平台在加剧社会、政治及民主进程中的极化现象方面发挥了关键作用，这主要源于其作为观点形成的媒介。观点动态模型作为理解特定社会因素对观点接受/拒绝影响的工具，可用于分析关于人类行为的特定假设。本研究提出一个框架，该框架以并发集合关系为形式基础，对具有动态观点模型的社会交互系统进行规约、仿真与分析。标准社会学习模型可作为所提框架的特例。该框架已在Maude系统中实现为完全可执行的重写理论，可用于更深入地理解智能体系统中观点的形成机制。本文还初步探索了在Maude中运用可达性分析、概率仿真及统计模型检测等方法对观点动态模型相关重要性质的研究。