ArticleFoods (Basel, Switzerland)2023
A Novel Foodborne Illness Detection and Web Application Tool Based on Social Media.
Article in Foods (Basel, Switzerland), 2023. 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.
- Artificial intelligence in food safety.NPJ science of food · 2026Review
- Lateral flow assay for detecting aflatoxin B1 based on prussian blue nanoparticles.Mikrochimica acta · 2026Article
- Ethical Considerations for the Use of Social Media in the Human Subjects Research Setting.Journal of medical Internet research · 2025Article
- Comparative antimicrobial activity of Zataria multiflora essential oil nanoformulations against foodborne pathogens.Scientific reports · 2025Article
- Applications of Artificial Intelligence in Food Industry.Foods (Basel, Switzerland) · 2025Article
- Foodborne Event Detection Based on Social Media Mining: A Systematic Review.Foods (Basel, Switzerland) · 2025Article
- Evolutionary Trends in the Adoption, Adaptation, and Abandonment of Mobile Health Technologies: Viewpoint Based on 25 Years of Research.Journal of medical Internet research · 2024Article
- Developing a digital data platform for surveillance of food and water-borne pathogens in North East India: insight for public health advocacy.Frontiers in public health · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Foodborne diseases and outbreaks are significant threats to public health, resulting in millions of illnesses and deaths worldwide each year. Traditional foodborne disease surveillance systems rely on data from healthcare facilities, laboratories, and government agencies to monitor and control outbreaks. Recently, there is a growing recognition of the potential value of incorporating social media data into surveillance systems. This paper explores the use of social media data as an alternative surveillance tool for foodborne diseases by collecting large-scale Twitter data, building food safety data storage models, and developing a novel frontend foodborne illness surveillance system. Descriptive and predictive analyses of the collected data were conducted in comparison with ground truth data reported by the U.S. Centers for Disease Control and Prevention (CDC). The results indicate that the most implicated food categories and the distributions from both Twitter and the CDC were similar. The system developed with Twitter data could complement traditional foodborne disease surveillance systems by providing near-real-time information on foodborne illnesses, implicated foods, symptoms, locations, and other information critical for detecting a potential foodborne outbreak.
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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.