ArticleScientific reports2021
Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 31 citations in OpenAlex.
- Integrating Statistical and Machine-Learning Approaches forMicroorganisms · 2025Article
- How big data analytics can strengthen large-scale food fortification and biofortification decision-making: A scoping review.Annals of the New York Academy of Sciences · 2025Article
- Data collection in fragile states.Nature food · 2025Article
- Applications of Machine Learning in Food Safety and HACCP Monitoring of Animal-Source Foods.Foods (Basel, Switzerland) · 2025Review
- Foodborne Event Detection Based on Social Media Mining: A Systematic Review.Foods (Basel, Switzerland) · 2025Article
- Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach.Journal of medical Internet research · 2023Article
- A Novel Foodborne Illness Detection and Web Application Tool Based on Social Media.Foods (Basel, Switzerland) · 2023Article
- An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain.Computational intelligence and neuroscience · 2022Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Foodborne outbreaks are a serious but preventable threat to public health that often lead to illness, loss of life, significant economic loss, and the erosion of consumer confidence. Understanding how consumers respond when interacting with foods, as well as extracting information from posts on social media may provide new means of reducing the risks and curtailing the outbreaks. In recent years, Twitter has been employed as a new tool for identifying unreported foodborne illnesses. However, there is a huge gap between the identification of sporadic illnesses and the early detection of a potential outbreak. In this work, the dual-task BERTweet model was developed to identify unreported foodborne illnesses and extract foodborne-illness-related entities from Twitter. Unlike previous methods, our model leveraged the mutually beneficial relationships between the two tasks. The results showed that the F1-score of relevance prediction was 0.87, and the F1-score of entity extraction was 0.61. Key elements such as time, location, and food detected from sentences indicating foodborne illnesses were used to analyze potential foodborne outbreaks in massive historical tweets. A case study on tweets indicating foodborne illnesses showed that the discovered trend is consistent with the true outbreaks that occurred during the same period.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.