Understanding Social Dynamics Through Coevolving Latent Space Networks With Attractors

这项研究项目将开发一类通用的协同进化网络模型。在社会系统中，互动经常影响个人的行为和信念，而这反过来又会影响互动。这种共同进化现象的具体变体包括意见动态、选民行为、观察性学习、羊群或蜂拥而至，以及两极分化。基于网络的模型很自然地代表了这种现象，相关工作可以在数学和统计文献中找到(除其他外)。然而，与其他类型的网络(例如，静态网络)的模型相比，协同进化网络模型的开发程度要低得多，并且它们更难以分析和理解。这个项目将开发一个模型类，它集成了数学和统计协同进化网络建模文献的中心元素。这些模型将被用来研究两个在线社交网络数据集的两极分化：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。研究生将接受这些领域的跨学科培训。这一研究项目将开发一类新的具有吸引子的共进化潜在空间网络(CLSNA)模型。CLSNA模型类的开发将导致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吸引和排斥等社会动态的数学精确概念纳入其中。后者将允许在计算上易于处理和理论上得到支持的统计推断方法。在这个项目中，研究人员将：(I)开发CLSNA模型的建模和统计推理方法，重点是群集和极化；(Ii)通过数值和数学技术的结合，研究这一类别允许的结果行为；以及(Iii)使用这些模型在在线社交媒体数据中对特定共同进化行为的性质和程度进行经验评估。将要开发的模型将是通用的，并且适用范围相当广泛。然而，调查人员计划利用在线社交网络数据集将最初的申请集中在情感两极分化的背景下。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.