Opinion Formation and Graph Dynamics: From Modeling to Empirical Applications

社交媒体的兴起彻底改变了我们对舆论形成的观念。现在，许多人通过社交平台上的互动获取信息，而不是一些中央渠道（电视频道、报纸）作为共同的外部参考框架。因此，舆论形成的动态已经变得更加自组织，大规模的行为是在没有中央权威的地方互动的结果，类似于一群鸟或一群鱼。但是，社交网络中的观点是如何形成的呢？社交网络的结构是对用户意见的反应，还是网络塑造了用户的意见？这个项目的主要动机之一是以下悖论：虽然社交媒体的使用增加了个人之间的联系，但观点变得更加两极分化；同性恋的“回音室”似乎是社交媒体结构中无处不在的特征。这些观察到的行为要求我们仔细研究社交媒体互动与大规模舆论形成动态之间的相互作用，以便更好地理解两极分化和回声室的兴起。该项目还将为研究生提供研究和指导机会。作为一种突发行为，意见形成的潜在动力是未知的，不能从物理原理中推导出来。因此，本项目开发的框架核心是跨学科的，结合了数学建模（动力系统、图论）和数据采集和分析（自然语言处理、情感分析、聚类算法）。这个项目将研究新的动态系统建模图和意见系统之间的相互作用。其目的是描述导致两极分化、共识或在这两种状态之间过渡的条件。然后，使用各种数据捕获技术从各大社交媒体平台提取实验数据。由此产生的测量结果将允许调查社交媒体的网络结构及其用户意见的分布如何随着时间的推移而变化，从而深入了解这两种动态之间的相互作用。最终目标是用实验数据来完善建模过程，以研究是什么驱动了观测到的极化趋势。最后，使用经过实证数据验证的模型，该项目将研究旨在减少两极分化和增强大量受众中各种意见自然流动的各种策略。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。

The rise of social media has drastically changed our conception of opinion formation. Rather than a few central outlets (TV channels, newspapers) acting as a common and external frame of reference, many now derive their information through interaction on social platforms. Therefore, the dynamics of opinion formation have become more self-organized where large scale behaviors emerge as a result of local interactions without a central authority, similar to a flock of birds or a shoal of fish. But how are opinions formed in social networks? Is the structure of social networks a reaction of the users' opinions or does the network shape the users' opinions? One of the key motivations for this project is the following paradox: while the use of social media has supposedly increased the connectivity among individuals, opinions have become more polarized; homophilic "echo chambers" appear to be a ubiquitous feature of the structure of social media. These observed behaviors call for a close examination of the interplay between social media interaction and the dynamics of opinion formation at scale in order to better understand the rise of polarization and echo chambers. This project will also provide research and mentorship opportunities for graduate students. As an emergent behavior, the underlying dynamics of opinion formation is unknown and cannot be derived from physical principles. Thus, the framework developed in this project is interdisciplinary at its core and combines both mathematical modeling (dynamical systems, graph theory) and data acquisition and analysis (natural language processing, sentiment analysis, clustering algorithms). This project will investigate novel dynamical systems modeling the interplay between a graph and a system of opinions. The aim is to characterize conditions resulting in polarization, consensus, or a transition between these two states. Then, experimental data will be extracted from the major social media platforms using a variety of data capture techniques. The resulting measurements will allow for an investigation of how the network structure of social media and the distribution of its user's opinions change overtime, providing insight into the interplay between these two dynamics. The ultimate goal is to refine the modeling pursuit with experimental data in order to investigate what is driving the observed trend towards polarization. Finally, using models validated with empirical data, the project will investigate various strategies aimed at reducing polarization and enhancing the natural flow of various opinions within a large audience.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.