  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.
