TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks

TL;DR

TWEET-FID is the first publicly available annotated Twitter dataset designed for multiple foodborne illness detection tasks, enabling the development of machine learning models to identify outbreaks from social media data.

Contribution

The paper introduces TWEET-FID, a novel annotated dataset for foodborne illness detection on Twitter, along with methodology and baseline results for related NLP tasks.

Findings

State-of-the-art models achieve promising results on TWEET-FID

Dataset supports multiple detection tasks including relevance, entity, and slot filling

Provides a foundation for future research in outbreak detection from social media

Abstract

Foodborne illness is a serious but preventable public health problem -- with delays in detecting the associated outbreaks resulting in productivity loss, expensive recalls, public safety hazards, and even loss of life. While social media is a promising source for identifying unreported foodborne illnesses, there is a dearth of labeled datasets for developing effective outbreak detection models. To accelerate the development of machine learning-based models for foodborne outbreak detection, we thus present TWEET-FID (TWEET-Foodborne Illness Detection), the first publicly available annotated dataset for multiple foodborne illness incident detection tasks. TWEET-FID collected from Twitter is annotated with three facets: tweet class, entity type, and slot type, with labels produced by experts as well as by crowdsource workers. We introduce several domain tasks leveraging these three facets: text relevance classification (TRC), entity mention detection (EMD), and slot filling (SF). We describe the end-to-end methodology for dataset design, creation, and labeling for supporting model development for these tasks. A comprehensive set of results for these tasks leveraging state-of-the-art single- and multi-task deep learning methods on the TWEET-FID dataset are provided. This dataset opens opportunities for future research in foodborne outbreak detection.