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