Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media

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 post...

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Autores principales:	Dandan Tao, Dongyu Zhang, Ruofan Hu, Elke Rundensteiner, Hao Feng
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/687c6d57923345e88d848bcfb42e1505
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spelling	oai:doaj.org-article:687c6d57923345e88d848bcfb42e15052021-11-08T10:50:50ZCrowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media10.1038/s41598-021-00766-w2045-2322https://doaj.org/article/687c6d57923345e88d848bcfb42e15052021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-00766-whttps://doaj.org/toc/2045-2322Abstract 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.Dandan TaoDongyu ZhangRuofan HuElke RundensteinerHao FengNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dandan Tao Dongyu Zhang Ruofan Hu Elke Rundensteiner Hao Feng Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media
description	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.
format	article
author	Dandan Tao Dongyu Zhang Ruofan Hu Elke Rundensteiner Hao Feng
author_facet	Dandan Tao Dongyu Zhang Ruofan Hu Elke Rundensteiner Hao Feng
author_sort	Dandan Tao
title	Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media
title_short	Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media
title_full	Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media
title_fullStr	Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media
title_full_unstemmed	Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media
title_sort	crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/687c6d57923345e88d848bcfb42e1505
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Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media

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