Signal Detection from Social Media
This project aims to extract features or signals from social media text that are relevant to disease outbreak and at the same time identifying fake signals. We will apply natural language processing techniques, such as linguistic feature extraction, event detection, and deep neural network-based feature extraction, to identify potential signals for disease outbreak. We will investigate machine learning methods for predicting disease outbreak given the identified signals or features.
  • Co-Project Investigators: Aijun An, Jude Dzevela Kong and Manos Papagelis (York University)
Networks »Networks
OMNI – Research »OMNI – Research
03 Early Warning Systems of Infectious Diseases »03 Early Warning Systems of Infectious Diseases
Signal Detection from Social Media
Jude Kong »Jude Kong
Manos Papagelis »Manos Papagelis
Aijun An »Aijun An
Neural networks »Neural networks
230511 Digital Disease Surveillance System »230511 Digital Disease Surveillance System
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