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 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,

SUBMITTER: Tao D 

PROVIDER: S-EPMC8568976 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature