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

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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.