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|Project name||Organization name||Social impact area||Project summary||Task name||Task status|
|Cloud Enabled Social Media Analytics Framework for Crisis Management||Artificial Intelligence and Data Engineering (AIDE)Research Center||Education||This project aims to develop a cloud-based computing framework that will systematically monitor, collect and integrate disaster and crisis-related data streams from diverse social network channels, and turn them into actionable intelligence in a knowledge-base for intelligent inferencing, crisis handling, and decision making. The key components of the proposed framework are three: First, the collection of multilingual and multimodal data (text, audios, videos, images, and location information or other sensor-data) and their conversion into a unified machine-understandable dynamic knowledge-base. Second, the application of Natural Language Processing (NLP), Data Mining, Machine Learning, and Artificial Intelligence techniques for automatic identification, prediction, and detection of any type of crisis or disaster using the unified knowledge-base from the previous stage. 3) Third, the development of an interactive dashboard visualization to facilitate crisis monitoring and management.||Project scoping||Started|
|GraphLab and IDDP Mapping the Spread||The George Washington University School of Engineering and Applied Science||Education||Dr. Howie Huang’s GraphLab, together with Dr. David Broniatowski and IDDP (Institute for Data, Democracy, and Politics) researchers, is working to map the prevalence and spread of attitudes towards COVID-19 on online social media platforms. They are examining repositories of social media data related to the epidemic, starting as early as January 2020, to map the spread of discussion, information, and attitudes towards the disease and the public health response to the outbreak.||Project scoping||Pending review|