  Foodborne illnesses significantly impact public health. Deep learning
surveillance applications using social media data aim to detect early warning
signals. However, labeling foodborne illness-related tweets for model training
requires extensive human resources, making it challenging to collect a
sufficient number of high-quality labels for tweets within a limited budget.
The severe class imbalance resulting from the scarcity of foodborne
illness-related tweets among the vast volume of social media further
exacerbates the problem. Classifiers trained on a class-imbalanced dataset are
biased towards the majority class, making accurate detection difficult. To
overcome these challenges, we propose EGAL, a deep learning framework for
foodborne illness detection that uses small expert-labeled tweets augmented by
crowdsourced-labeled and massive unlabeled data. Specifically, by leveraging
tweets labeled by experts as a reward set, EGAL learns to assign a weight of
zero to incorrectly labeled tweets to mitigate their negative influence. Other
tweets receive proportionate weights to counter-balance the unbalanced class
distribution. Extensive experiments on real-world \textit{TWEET-FID} data show
that EGAL outperforms strong baseline models across different settings,
including varying expert-labeled set sizes and class imbalance ratios. A case
study on a multistate outbreak of Salmonella Typhimurium infection linked to
packaged salad greens demonstrates how the trained model captures relevant
tweets offering valuable outbreak insights. EGAL, funded by the U.S. Department
of Agriculture (USDA), has the potential to be deployed for real-time analysis
of tweet streaming, contributing to foodborne illness outbreak surveillance
efforts.
