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Foodborne illnesses significantly impact public health. Deep learningsurveillance applications using social media data aim to detect early warningsignals. However, labeling foodborne illness-related tweets for model trainingrequires extensive human resources, making it challenging to collect asufficient number of high-quality labels for tweets within a limited budget.The severe class imbalance resulting from the scarcity of foodborneillness-related tweets among the vast volume of social media furtherexacerbates the problem. Classifiers trained on a class-imbalanced dataset arebiased towards the majority class, making accurate detection difficult. Toovercome these challenges, we propose EGAL, a deep learning framework forfoodborne illness detection that uses small expert-labeled tweets augmented bycrowdsourced-labeled and massive unlabeled data. Specifically, by leveragingtweets labeled by experts as a reward set, EGAL learns to assign a weight ofzero to incorrectly labeled tweets to mitigate their negative influence. Othertweets receive proportionate weights to counter-balance the unbalanced classdistribution. Extensive experiments on real-world \textit{TWEET-FID} data showthat EGAL outperforms strong baseline models across different settings,including varying expert-labeled set sizes and class imbalance ratios. A casestudy on a multistate outbreak of Salmonella Typhimurium infection linked topackaged salad greens demonstrates how the trained model captures relevanttweets offering valuable outbreak insights. EGAL, funded by the U.S. Departmentof Agriculture (USDA), has the potential to be deployed for real-time analysisof tweet streaming, contributing to foodborne illness outbreak surveillanceefforts.

UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection