Balanced and Explainable Social Media Analysis for Public Health with
  Large Language Models
Yan Jiang Ruihong Qiu Yi Zhang Peng-Fei Zhang
As social media becomes increasingly popular, more and more public health
activities emerge, which is worth noting for pandemic monitoring and government
decision-making. Current techniques for public health analysis involve popular
models such as BERT and large language models (LLMs). Although recent progress
in LLMs has shown a strong ability to comprehend knowledge by being fine-tuned
on specific domain datasets, the costs of training an in-domain LLM for every
specific public health task are especially expensive. Furthermore, such kinds
of in-domain datasets from social media are generally highly imbalanced, which
will hinder the efficiency of LLMs tuning. To tackle these challenges, the data
imbalance issue can be overcome by sophisticated data augmentation methods for
social media datasets. In addition, the ability of the LLMs can be effectively
utilised by prompting the model properly. In light of the above discussion, in
this paper, a novel ALEX framework is proposed for social media analysis on
public health. Specifically, an augmentation pipeline is developed to resolve
the data imbalance issue. Furthermore, an LLMs explanation mechanism is
proposed by prompting an LLM with the predicted results from BERT models.
Extensive experiments conducted on three tasks at the Social Media Mining for
Health 2023 (SMM4H) competition with the first ranking in two tasks demonstrate
the superior performance of the proposed ALEX method. Our code has been
released in https://github.com/YanJiangJerry/ALEX.