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，因为误导性陈述会像病毒一样传播。 社交媒体的公民承诺是让人们普遍获得所需的知识，为有争议的问题提供信息并激励广泛的政治参与。 相反，社交媒体已经成为认知两极分化的培养皿--将公众分为红色和蓝色现实。 在社交媒体上传播两极分化的无法核实的声明，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式传播方式发起有毒的传播活动。 通过分析新旧社交媒体数据和开发新的分析方法，该项目的研究使人们更好地了解误导性陈述的传播动态。这将有助于提高网络空间的可信度。使用以前收集的十年Twitter档案，沿着新的数据收集，策划和匿名化，在这个项目中的极化的影响，在网上网络误导性陈述的传播进行了调查。新的数据集将是同类数据中最大的公开数据集，有助于今后对这一主题的研究。此外，分析方法的开发，以评估的意识形态内容，信息的准确性，用户的对齐，以及误导性声明级联的结构和时间特征，如到达，速度和病毒式传播。这项工作的结果是对误导性陈述的扩散动力学有了更深入的理解。这可以为早期遏制对策提供信息，从而在传染病蔓延失控之前更有效地识别、跟踪和中和扩散。 该项目由社会学计划、人类网络和数据科学-研究（HNDS-R）计划以及安全和值得信赖的网络空间（SaTC）计划共同资助。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 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.