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.

Creator

• Rathke, Liam
• Laber, Jasmine
• Hartmann, Katy

Subject

• Health
• Food
• Project-Based Learning

Publisher

• Worcester Polytechnic Institute

Identifier

• 94691
• E-project-032323-161056

Keyword

• twitter
• social media
• usda
• food poisoning

Advisor

• Rundensteiner, Elke A.
• Mangoubi, Oren Rami

Year

• 2023

UN Sustainable Development Goals

• 3 - Good Health and Well-being
• 12 - Responsible Consumption and Production

Date created

• 2023-03-23

Resource type

• Major Qualifying Project

Major

• Data Science
• Computer Science

Source

• E-project-032323-161056

Rights statement

• In Copyright

Last modified

• 2023-04-14