EAGER: An AI-driven Paradigm for Collective and Collaborative Community Resilience in the COVID-19 Era and Beyond

冠状病毒病(新冠肺炎)大流行暴露了一组严重的漏洞，这些漏洞影响了社区应对不断升级的社会、经济和行为问题的应变能力。不幸的是，没有既定的解决方案或经过验证的模式可供我们依靠，以应对具有重大不确定性和未知数的复杂挑战。该项目采用新的学科观点来帮助应对新冠肺炎造成的破坏性影响，即利用从正能量社区提取的经验、想法和支持的信息，这些社区正在成功地驾驭威胁，这些威胁可以转化为可操作的信息，以帮助脆弱社区应对、进步和前进。更具体地说，通过推进人工智能(AI)创新，该项目的目标是设计和开发一种人工智能驱动的范式，用于集体和协作的社区韧性，以应对新冠肺炎时代及以后的各种危机和暴露的漏洞。通过进一步的验证，这项研究将为帮助联邦和州政府、公司、社会领导人制定和实施战略提供基础，这些战略将引导当地和地区社区以及国家进入成功的新常态未来。这项探索性但具有变革性的高风险-高回报工作涉及截然不同的方法，将由三个主要研究部分组成。首先，研究小组将构建一个新的属性异质信息网络(Ahin)，对最新的多源大流行相关数据进行综合建模，以进行抽象表示。其次，为了了解用户如何交互以及信息在社交媒体中如何在社区内和跨社区传播，该团队将考虑到网络的异质性，开发一种创新的非负矩阵分解正则化深度图学习模型，用于AHIN中的社区检测。第三，团队将提出一个综合的对抗性解缠者，以分离隐藏在环境中的不同的、信息丰富的变化因素，以学习嵌入情感的帖子和用于社区分类和框架的话题分析，从而获得支持性和建设性的信息，以提高社区的复原力。该项目中开发的人工智能驱动的范式将提供深入的见解和定制的指导，可以帮助公共卫生专家、社会工作者、执法部门、经济学家和政策制定者进行决策，还可以为制定弹性社区参与战略提供概念框架，以应对新冠肺炎造成的各种危机和未来的自然或健康相关灾难。这项研究将有益于多学科领域，包括数据挖掘、机器学习、流行病学、经济学、社会学和行为科学。该项目的成果将向公众公布并广泛分发。该项目将通过课程开发、代表性不足的团体的参与和学生指导活动将研究与教育相结合。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 coronavirus disease (COVID-19) pandemic has exposed a critical set of vulnerabilities that have impacted community resilience in responding to escalating societal, economic, and behavioral issues. Unfortunately, there are no established solutions or proven models for us to depend on to tackle the complex challenges with significant uncertainties and unknowns. This project engages novel disciplinary perspectives to help address the devastating effects caused by COVID-19, i.e., leveraging the extracted information of experiences, ideas and support from positive-energy communities who are successfully navigating threats that can be transformed and transferred into actionable information to assist vulnerable communities to cope, progress and move forward. More specifically, by advancing artificial intelligence (AI) innovations, the goal of this project is to design and develop an AI-driven paradigm for collective and collaborative community resilience in responses to a variety of crises and exposed vulnerabilities in the COVID-19 era and beyond. With additional validation, this research will provide foundation to assist the federal and state governments, corporations, societal leaders to develop and implement strategies that will guide local and regional communities, and the nation into a successful new normal future.This exploratory yet transformative high risk-high payoff work that involves radically different approaches will have three main research components. First, the research team will construct a novel attributed heterogeneous information network (AHIN) to comprehensively model the up-to-date multi-source pandemic related data for abstract representation. Second, to understand how users interact and how information are propagated within and cross-community in social media, the team will develop an innovative nonnegative matrix factorization regularized deep graph learning model for community detection in the AHIN by considering the heterogeneity of the network. Third, the team will propose an integrated adversarial disentangler to separate the distinct, informative factors of variations hidden in the milieu to learn post embeddings for emotion and topic analysis for community classification and framing, and thus to derive supportive and constructive information for community resilience improvement. The developed AI-driven paradigm in this project will provide in-depth insights and customized guidance that can help public health experts, social workers, law enforcement, economists, and policy makers in decision-making and also enable a conceptual framework for the development of resilient community engagement strategies in responses to a variety of crises created by COVID-19 and future natural or health-related disasters. The research will be beneficial to multidisciplinary areas, including data mining, machine learning, epidemiology, economics, social and behavioral sciences. The outcomes of this project will be made publicly accessible and broadly distributed. The project will integrate research with education through curriculum development, the participation of underrepresented groups, and student mentoring activities.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.