Understanding Social Dynamics Through Coevolving Latent Space Networks With Attractors

通过与吸引子共同演化的潜在空间网络来理解社会动态

• 批准号: 2120115
• 负责人: Eric Kolaczyk
• 金额: $ 45万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2120115&HistoricalAwards=false
• 关键词: Understanding; Social; Dynamics; Through; Coevolving

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.

本研究计画将发展一个一般性的共同演化网路模型。在社会系统中，相互作用经常影响个人的行为和信念，这反过来又会影响相互作用。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，和极化。基于网络的模型很自然地代表了这些现象，相关的工作可以在数学和统计文献中找到（等等）。然而，协同进化网络模型远不如其他类型的网络模型（例如，静态网络），并且它们的分析和理解更加复杂。这个项目将开发一个模型类，它集成了数学和统计共同进化网络建模文献的核心元素。这些模型将用于研究两个在线社交网络数据集的两极分化，即Twitter for Congress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培训。本研究计划将发展一种新的社会系统的具有吸引子的共同演化潜在空间网络（CLSNA）模型。 CLSNA模型类的开发将导致一种新型的因果建模框架，明确地将数学中的动态系统建模与分层建模和统计推断相结合。前者将允许研究人员将数学上精确的社会动力学概念，如吸引和排斥。后者将允许计算上易于处理和理论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断方法，重点是群集和极化;（ii）通过数字和数学技术的结合，研究这类允许的结果行为;（iii）使用这些模型在在线社交媒体数据中经验性地评估特定共同进化行为的性质和程度。将要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集中在使用在线社交网络数据集的情感极化的背景下。这个奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Disentangling positive and negative partisanship in social media interactions using a coevolving latent space network with attractors model

使用具有吸引子模型的共同演化潜在空间网络来消除社交媒体互动中的积极和消极党派之争

• DOI: 10.1093/jrsssa/qnad008
• 发表时间: 2023
• 期刊: Journal of the Royal Statistical Society Series A: Statistics in Society
• 作者: Zhu, Xiaojing;Caliskan, Cantay;Christenson, Dino P.;Spiliopoulos, Konstantinos;Walker, Dylan;Kolaczyk, Eric D.
• 通讯作者: Kolaczyk, Eric D.