  Detecting diseases from social media has diverse applications, such as public
health monitoring and disease spread detection. While language models (LMs)
have shown promising performance in this domain, there remains ongoing research
aimed at refining their discriminating representations. In this paper, we
propose a novel method that integrates Contrastive Learning (CL) with language
modeling to address this challenge. Our approach introduces a self-augmentation
method, wherein hidden representations of the model are augmented with their
own representations. This method comprises two branches: the first branch, a
traditional LM, learns features specific to the given data, while the second
branch incorporates augmented representations from the first branch to
encourage generalization. CL further refines these representations by pulling
pairs of original and augmented versions closer while pushing other samples
away. We evaluate our method on three NLP datasets encompassing binary,
multi-label, and multi-class classification tasks involving social media posts
related to various diseases. Our approach demonstrates notable improvements
over traditional fine-tuning methods, achieving up to a 2.48% increase in
F1-score compared to baseline approaches and a 2.1% enhancement over
state-of-the-art methods.
