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

Ruofan Hu, Dongyu Zhang, Dandan Tao, Thomas Hartvigsen, Hao Feng, Elke Rundensteiner


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.
Anthology ID:
2022.lrec-1.668
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6212–6222
Language:
URL:
https://aclanthology.org/2022.lrec-1.668
DOI:
Bibkey:
Cite (ACL):
Ruofan Hu, Dongyu Zhang, Dandan Tao, Thomas Hartvigsen, Hao Feng, and Elke Rundensteiner. 2022. TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6212–6222, Marseille, France. European Language Resources Association.
Cite (Informal):
TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks (Hu et al., LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.668.pdf