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As social media becomes increasingly popular, more and more activitiesrelated to public health emerge. Current techniques for public health analysisinvolve popular models such as BERT and large language models (LLMs). However,the costs of training in-domain LLMs for public health are especiallyexpensive. Furthermore, such kinds of in-domain datasets from social media aregenerally imbalanced. To tackle these challenges, the data imbalance issue canbe overcome by data augmentation and balanced training. Moreover, the abilityof the LLMs can be effectively utilized by prompting the model properly. Inthis paper, a novel ALEX framework is proposed to improve the performance ofpublic health analysis on social media by adopting an LLMs explanationmechanism. Results show that our ALEX model got the best performance among allsubmissions in both Task 2 and Task 4 with a high score in Task 1 in SocialMedia Mining for Health 2023 (SMM4H)[1]. Our code has been released at https://github.com/YanJiangJerry/ALEX.

UQ at #SMM4H 2023: ALEX for Public Health Analysis with Social Media