Discovering foodborne illness in online restaurant reviews

Abstract

Our system has been instrumental in the identification of 10 outbreaks and 8523 complaints of foodborne illness associated with New York City restaurants since July 2012. Our evaluation has identified strong classifiers for both tasks, whose deployment will allow DOHMH epidemiologists to more effectively monitor Yelp for foodborne illness investigations.

Utilization of text mining as a big data analysis tool for food science and nutrition

“…Popular applications of text classification in our daily life includes spam detection, opinion mining, and information filtering. In the food industry, for example, text classification has been used to identify unreported cases of foodborne illnesses from social media (Effland et al., ; Harris et al., ; Harrison et al., ; Sadilek et al., ). Text clustering: Text clustering is used for grouping objects (e.g., documents, paragraphs, sentences or terms) based on similarity between the objects (Aggarwal & Zhai, ). There are a variety of algorithms used for text clustering depending on how to calculate the similarity.…”

“…Popular applications of text classification in our daily life includes spam detection, opinion mining, and information filtering. In the food industry, for example, text classification has been used to identify unreported cases of foodborne illnesses from social media (Effland et al, 2018;Harris et al, 2014;Harrison et al, 2014;Sadilek et al, 2016).…”

Using big data to promote precision oral health in the context of a learning healthcare system

“…This process of learning and intervening has already begun, utilizing social media. An innovative example of using big data analytics for targeted public health interventions was recently demonstrated by a team at Columbia University that developed a system that identifies foodborne illnesses in NYC restaurants by analyzing Yelp reviews . The system utilizes logistic regression trained with bias‐adjusted augmented data, which has identified 10 outbreaks and 8,253 complaints of foodborne illness associated with NYC restaurants since 2012.…”

“…An innovative example of using big data analytics for targeted public health interventions was recently demonstrated by a team at Columbia University that developed a system that identifies foodborne illnesses in NYC restaurants by analyzing Yelp reviews. 115 The system utilizes logistic regression trained with bias-adjusted augmented data, which has identified 10 outbreaks and 8,253 complaints of foodborne illness associated with NYC restaurants since 2012. Perhaps the best description of necessary pieces of a learning health care system was given by Dr. Friedman, who chairs the Department of Learning Health Sciences at the University of Michigan School of Medicine in Ann Harbor.…”

NYC HANES 2013–14 and Reflections on Future Population Health Surveillance

“…Public-private partnerships can encourage the use of advanced technology and have resulted in innovative approaches to capturing public health data. For example, the Centers for Disease Control and Prevention has recently partnered with Watson Health to explore ways to better manage data and respond to emergencies [23], and the NYC Health Department worked with Columbia University in using social media restaurant reviews to detect food poisoning outbreaks [24]. As such partnerships evolve, generating representative data that can inform public health agencies and policy-makers will be critical.…”