Collaborative Research: HNDS-R: Polarization, Information Integrity, and Diffusion

这个项目的目的是加深对两极分化的理解，因为两极分化是误导性言论迅速传播的漩涡。社交媒体的公民承诺是普遍获得所需的知识，以便为有争议的问题提供信息和推动广泛的政治参与。相反，社交媒体已经成为认知两极分化的培养皿--将公众划分为红色和蓝色的现实。两极分化的无法证实的言论在社交媒体上的传播侵蚀了建设性分歧所需的共享知识，并允许外国对手以可怕的病毒发起有毒的传播活动。通过对新旧社交媒体数据的分析和新分析方法的发展，本项目的研究对误导性陈述的传播动力学有了更深入的了解。这将有助于提高网络空间的可信度。利用之前收集的十年Twitter档案，以及需要收集、整理和匿名的新数据，在这个项目中，调查了两极分化对在线网络中误导性陈述传播的影响。新的数据集将是同类数据集中最大的可公开获得的数据集，有助于今后对这一主题的研究。此外，还开发了分析方法来评估误导性陈述级联的意识形态内容、信息的真实性、用户的一致性以及结构和时间特征，如覆盖范围、速度和病毒。这项工作的结果是对误导性陈述的传播动力学有了更深的理解。这可以为早期遏制对策提供信息，以便在传播失控之前更有效地识别、跟踪和中和扩散。该项目由社会学项目、人类网络和数据科学研究(HNDS-R)项目以及安全和值得信赖的网络空间(SATC)项目共同资助。该奖项反映了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。

The aim of this project is to deepen understanding of polarization as the vortex in which misleading statements go viral. The civic promise of social media was universal access to the knowledge needed to inform and motivate broad political engagement on controversial issues. Instead, social media have become the Petri dish for epistemic polarization – the division of the public into red and blue realities. The spread of polarizing unverifiable statements over social media erodes the shared knowledge needed for constructive disagreement and allows foreign adversaries to mount toxic communication campaigns with frightening virality. Through analysis of old and new social media data and development of new analytic methods, the research in this project brings a greater understanding of the diffusion dynamics of misleading statements. This will be useful in improving the trustworthiness of cyberspace.Using previously collected ten-year Twitter archive, along with new data to be collected, curated, and anonymized, in this project the impact of polarization on the dissemination of misleading statements in online networks is investigated. The new data set will be the largest publicly available data set of its kind, contributing to future research on this topic. In addition, analytical methods are developed to evaluate the ideological content, veracity of messages, alignment of users, and the structural and temporal characteristics of misleading statement cascades, such as reach, velocity, and virality. What results from this work is a deeper understanding of the diffusion dynamics of misleading statements. This can inform an early-containment counterstrategy that can more effectively identify, track, and neutralize diffusion before the contagion can spread out of control. This project is funded jointly by the Sociology Program, Human Networks and Data Science – Research (HNDS-R) Program, and Secure and Trustworthy Cyberspace (SaTC) Program.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.