CS/DS MQP: Learning Early Warning Alerts for Food Poisoning from Social MediaPublic Deposited
Downloadable Contentopen in viewer
This project aimed to create the groundwork for a tool that identifies foodborne illness cases from Twitter as the first steps in creating an unofficial warning system to slow the spread of foodborne illness. Collecting and storing historical tweets, creating effective visualizations were created to display the trends of foodborne illness over time, comparing Twitter data to official foodborne illness data, and evaluating the machine learning model used all contributed towards creating a framework for this early warning system.
- This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.
- UN Sustainable Development Goals
- Date created
- Resource type
- Rights statement
- Last modified
- In Collection:
Permanent link to this page: https://digital.wpi.edu/show/jw827g198