Understanding Social Dynamics Through Coevolving Latent Space Networks With Attractors

本研究计划将发展一类共同演化的网路模型。在社会系统中，相互作用经常影响个人的行为和信念，而这些行为和信念反过来又影响相互作用。这种类型的共同进化现象的具体变体包括意见动态、选民行为、观察学习、羊群或羊群以及两极分化。基于网络的模型对于表示这种现象是很自然的，相关的工作可以在数学和统计文献（以及其他文献）中找到。然而，与其他类型的网络（例如，静态网络）模型相比，共同进化的网络模型实质上发展得不够好，而且它们的分析和理解更加复杂。该项目将开发一个模型类，集成数学和统计协同进化网络建模文献的中心元素。这些模型将用于检查两个在线社交网络数据集的两极分化，即国会的Twitter和Reddit。该项目将涉及统计、数学、政治和计算社会科学家之间的合作。研究生将在这些领域接受跨学科的培训。将开发公开可用的软件。本研究项目将为社会系统开发一类具有吸引子的共同进化潜在空间网络（CLSNA）模型。CLSNA模型类的发展将产生一种新型的因果建模框架，明确地将来自数学的动态系统建模与来自统计的分层建模和推理相结合。前者将使研究人员能够将社会动力学的精确数学概念纳入其中，比如吸引力和排斥力。后者将允许计算上易于处理和理论上支持的统计推断方法。在本项目中，研究者将：(i)开发CLSNA模型的建模和统计推断方法，重点是群集和极化；（ii）通过数值和数学技术的结合，研究本课程所允许的结果行为；（iii）利用这些模型对在线社交媒体数据中特定共同进化行为的性质和程度进行实证评估。所开发的模型将是通用的，适用范围相当广泛。然而，研究人员计划将最初的应用重点放在使用在线社交网络数据集的情感极化背景上。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This research project will develop a general class of coevolving network models. In social systems, interactions frequently influence individual behavior and beliefs which can, in turn, impact interactions. Specific variants of this type of coevolutionary phenomenon include opinion dynamics, voter behavior, observational learning, herding or flocking, and polarization. Network-based models are natural for representing such phenomena, and relevant work can be found in both the mathematical and statistical literatures (among others). However, coevolving network models are substantially less well-developed than models for networks of other types (e.g., static networks) and they are more complex to analyze and understand. This project will develop a model class that integrates central elements of the mathematical and statistical coevolving network modeling literatures. The models will be used to examine polarization in two online social network data sets, Twitter for Congress and Reddit. The project will involve a collaboration between statistical, mathematical, political, and computational social scientists. Graduate students will receive cross-disciplinary training in these areas. Publicly available software will be developed.This research project will develop a general new class of coevolving latent space network with attractors (CLSNA) models for social systems. The development of the CLSNA model class will result in a new type of causal modeling framework, explicitly combining dynamical systems modeling from mathematics with hierarchical modeling and inference from statistics. The former will allow the investigators to incorporate mathematically precise notions of social dynamics, like attraction and repulsion. The latter will permit computationally tractable and theoretically supported methods for statistical inference. In this project, the investigators will: (i) develop the modeling and statistical inference methodology for CLSNA models, with an emphasis on flocking and polarization; (ii) study the resulting behaviors allowed by this class, through a combination of both numerical and mathematical techniques; and (iii) assess empirically in online social media data the nature and extent of specific coevolutionary behaviors using these models. The models to be developed will be general and quite broadly applicable. The investigators, however, plan to focus initial applications on the context of affective polarization using online social network data sets.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.