|Mining and Summarization of Early Warning Pandemic Signals|
Mining and Summarization of Early Warning Pandemic Signals for vector-borne diseases (Lyme and Chikungunya, etc.).
- This project aims to develop algorithms and tools to automatically detect early warning signals (EWS) for a pandemic in multiple available data sources like internet activity (e.g., Twitter, Facebook, etick.ca). Social media data and web-scraping are especially effective to detect and understand public sentiment for some infectious diseases (ID).We will investigate the design algorithms to detect signals from social media texts, e.g., detection of pandemic-related entities and events, tracing opinions about a particular event across multiple sources, and offering clues of contrastive viewpoint.
- Co-Project Investigators: Gias Uddin (University of Calgary), Bouchra Nasri (Université de Montréal), Jude Dzevela Kong (York University), and Mark Lewis (University of Alberta)