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.

该研究项目将开发一般的共同发展网络模型。在社会系统中，互动经常影响个人行为和信念，而这些行为和信念反过来又会影响互动。这种类型的协同进化现象的特定变体包括舆论动态，选民行为，观察性学习，畜牧或羊群以及极化。基于网络的模型是代表这种现象的自然模型，并且在数学和统计文献（以及其他）中都可以找到相关的工作。但是，在其他类型的网络（例如，静态网络）的网络模型中，协调网络模型的开发得多，并且更复杂，可以分析和理解。该项目将开发一个模型类，该类别集成了数学和统计协调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，即国会和Reddit的Twitter。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。将开发公开可用的软件。该研究项目将开发一类新的与社交系统吸引子（CLSNA）模型的潜在空间网络。 CLSNA模型类的开发将导致一种新型的因果建模框架，明确地结合了来自具有分层建模的数学的动力系统建模和来自统计信息的推理。前者将允许研究人员纳入数学上精确的社会动态概念，例如吸引力和排斥。后者将允许在计算上进行统计推断的方法和理论上支持的方法。在该项目中，研究人员将：（i）为CLSNA模型开发建模和统计推断方法，重点是植入和极化； （ii）通过数字和数学技术的结合来研究该类别所允许的结果行为； （iii）在在线社交媒体数据中进行经验评估使用这些模型的特定协同进化行为的性质和程度。要开发的模型将是一般的，并且非常广泛地适用。但是，调查人员计划使用在线社交网络数据集将最初的应用集中在情感两极分化的背景下。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。

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.