Opinion Dynamics is an interdisciplinary area of research. Psychology and Sociology have proposed models of how individuals form opinions and how social interactions influence this process. Socio-Physicists have interpreted patterns in opinion formation as arising from non-linearity in the underlying process, shaping the models. Agent-based modeling has offered a platform to study the Opinion Dynamics of large groups. This paper recasts recent models in opinion formation into a proper dynamical system, injecting the idea of clock time into evolving opinions. The time interval between successive receipts of new information (frequency of information receipts) becomes a factor to study. Social media has shrunk time intervals between information receipts, increasing their frequency. The recast models show that shorter intervals and larger networks increase an individual's propensity for polarization, defined as an inability to hold a neutral opinion. A Polarization number based on sociological parameters is proposed, with critical values beyond which individuals are prone to polarization, depending on psychological parameters. Reduced time intervals and larger interacting groups can push the Polarization number to critical values, contributing to polarization. The Extent of Polarization is defined as the width of the region around neutral within which an individual cannot hold an opinion. Results are reported for model parameters found in the literature. The findings offer an opportunity to adjust model parameters to align with empirical evidence, aiding the study of Opinion Dynamics in large social networks using Agent-Based Modeling.

翻译：观点动力学是一个跨学科研究领域。心理学和社会学提出了关于个体如何形成观点以及社会互动如何影响这一过程的模型。社会物理学家将观点形成模式解释为源于底层过程的非线性特征，从而塑造了这些模型。基于智能体的建模为研究大规模群体的观点动力学提供了平台。本文将近期观点形成模型重构为适当的动力系统，将时钟时间概念引入观点演化过程。连续接收新信息的时间间隔（信息接收频率）成为一个研究因素。社交媒体缩短了信息接收的时间间隔，提高了信息接收频率。重构模型表明，较短的时间间隔和较大的网络规模会增加个体极化的倾向，极化被定义为无法持有中立观点。本文提出了基于社会学参数的极化数，其临界值取决于心理学参数，超过该临界值个体易发生极化。缩短的时间间隔和扩大的互动群体可能将极化数推至临界值，从而加剧极化。极化程度被定义为个体无法持有观点的中立区域周围的宽度范围。本文报告了文献中模型参数的结果。这些发现为调整模型参数以与经验证据保持一致提供了机会，有助于利用基于智能体的建模方法研究大规模社交网络中的观点动力学。