An ever-increasing amount of social media content requires advanced AI-based
computer programs capable of extracting useful information. Specifically, the
extraction of health-related content from social media is useful for the
development of diverse types of applications including disease spread,
mortality rate prediction, and finding the impact of diverse types of drugs on
diverse types of diseases. Language models are competent in extracting the
syntactic and semantics of text. However, they face a hard time extracting
similar patterns from social media texts. The primary reason for this shortfall
lies in the non-standardized writing style commonly employed by social media
users. Following the need for an optimal language model competent in extracting
useful patterns from social media text, the key goal of this paper is to train
language models in such a way that they learn to derive generalized patterns.
The key goal is achieved through the incorporation of random weighted
perturbation and contrastive learning strategies. On top of a unique training
strategy, a meta predictor is proposed that reaps the benefits of 5 different
language models for discriminating posts of social media text into non-health
and health-related classes. Comprehensive experimentation across 3 public
benchmark datasets reveals that the proposed training strategy improves the
performance of the language models up to 3.87%, in terms of F1-score, as
compared to their performance with traditional training. Furthermore, the
proposed meta predictor outperforms existing health mention classification
predictors across all 3 benchmark datasets.

通过随机加权干扰和对比学习策略培训语言模型，并提出元预测器以区分社交媒体文本中的非健康和健康相关类别，实验证明该策略提高了语言模型的性能，元预测器在三个基准数据集上优于现有的健康提及分类预测器。