Opinion Formation and Graph Dynamics: From Modeling to Empirical Applications

意见形成和图形动态：从建模到实证应用

基本信息

批准号：
2206330
负责人：
Sebastien Motsch
金额：
$ 41.69万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2206330&HistoricalAwards=false
关键词：
Opinion Formation Graph Dynamics Modeling

项目摘要

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.

社交媒体的兴起彻底改变了我们对舆论形成的概念。现在，许多人现在通过社交平台上的互动来得出他们的信息，而不是一些中央媒体（电视频道，报纸）充当常见和外部参考框架。因此，舆论形成的动力已经变得更加自组织，而大规模行为由于没有中央权威的局部互动而出现，类似于一群鸟类或一群鱼类。但是，社交网络中如何形成意见？社交网络的结构是用户意见的反应还是网络塑造了用户的意见？该项目的关键动机之一是以下悖论：虽然社交媒体的使用据说可以提高个人之间的连通性，但观点却变得更加两极化；同质的“回声室”似乎是社交媒体结构的无处不在的特征。这些观察到的行为要求对社交媒体互动与舆论形成动态之间的相互作用进行仔细检查，以便更好地了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。作为一种紧急行为，意见形成的基本动力是未知的，不能源自物理原理。因此，该项目中开发的框架在其核心上是跨学科的，并结合了数学建模（动力学系统，图理论）和数据采集和分析（自然语言处理，情感分析，聚类算法）。该项目将调查新的动力系统，以建模图和观点系统之间的相互作用。目的是表征导致两种状态之间极化，共识或过渡的条件。然后，将使用各种数据捕获技术从主要的社交媒体平台中提取实验数据。最终的测量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流逝而变化，从而洞悉这两个动态之间的相互作用。最终目标是通过实验数据来完善建模追求，以研究驱动观察到的极化趋势的原因。最后，使用经过经验数据验证的模型，该项目将调查旨在减少大型受众群体中各种观点自然流动的各种策略。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智力优点和更广泛影响的审查标准通过评估来进行评估的。