EAGER: DCL: SaTC: Enabling Interdisciplinary Collaboration: Combatting Disinformation and Racial Bias: A Deep-Learning-Assisted Investigation of Temporal Dynamics of Disinformation

这个项目探讨了网上种族虚假信息的传播及其社会影响，特别是对亚裔美国人的影响。虽然在新冠肺炎大流行期间，人们对亚裔美国人的仇恨和偏见已经变得明显，但针对亚裔的虚假信息尚未得到充分调查。该项目的创新之处在于采用独特的多学科方法(1)检测以亚洲人为目标的虚假信息及其对策信息，并了解它们如何在网络上传播，(2)研究虚假信息和对策信息在网络上的传播如何与针对亚裔美国人的偏见和仇恨犯罪的强度有关，以及(3)开发各种数据驱动的计算模型，以帮助理解虚假信息的动态。该项目的更广泛的意义和重要性是让民间社会，包括倡导组织和公众，了解如何在打击种族虚假信息方面制定传播努力的战略，并使开发的工具和成果公开供更广泛的使用。第一阶段的主要目标是开发计算工具，用于检测和分析以亚洲为目标的虚假信息与网上对策之间的时间动态。一个具体的重点是通过对虚假信息的独特特征进行特征工程来开发自动识别工具和深度学习分类模型。第二阶段的目标是了解网上虚假信息和对策的传播在多大程度上与现实世界中发生的针对亚裔美国人的隐性偏见和仇恨犯罪的社会趋势有关，这可以通过开发深度学习因果关系模型来实现。第三阶段的目标是在宏观和微观层面上设计可扩展的数据驱动的虚假信息动力学深度学习模型，从真实世界的测量中识别未知的动态，这也能够模拟已知的动态。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。

This project explores the diffusion of racial disinformation online and its social impacts, particularly focusing on Asian Americans. While the hatred and bias against Asian Americans have become notable amid the COVID-19 pandemic, Asian-targeting disinformation has yet been fully explored. The project's novelties are in unique multidisciplinary approaches to (1) detect Asian-targeting disinformation and its countermeasure messages, and understand how they are spread on the web, (2) examine how the spread of disinformation and countermeasure messages on the web is associated with the intensity of the bias and hate crimes against Asian Americans, and (3) develop various data-driven computational models to help understanding the disinformation dynamics. The project's broader significance and importance are to inform civil society, including advocacy organizations and the general public, about how to strategize communication efforts in battling racial disinformation, and to make the developed tools and outcomes publicly available for broader uses.The project takes three-staged approaches. The main objective of the first phase is to develop computational tools for the detection and analysis of the temporal dynamics between Asian-targeted disinformation and countermeasures on the Web. A specific focus is on developing automated identification tools and deep-learning classification models by feature-engineering unique characteristics of disinformation. The objective of the second phase is to understand to what extent the spread of disinformation and countermeasures online is associated with the societal trend of implicit bias and hate crime occurrences against Asian Americans in the real-world, which can be achieved via developing deep-learning causality models. The objective of the third phase is to design scalable data-driven deep-learning models of disinformation dynamics in macro and micro levels, identifying unknown dynamics from the real-world measurements, which also enables simulations of the learned dynamics.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.