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.

翻译：社交媒体内容日益增多，需要基于人工智能的高级计算机程序来提取有用信息。具体而言，从社交媒体中提取健康相关内容有助于开发多种应用，包括疾病传播、死亡率预测以及发现不同类型药物对各类疾病的影响。语言模型擅长提取文本的句法和语义信息，但在从社交媒体文本中提取类似模式时面临困难。这一不足的主要原因在于社交媒体用户普遍采用非标准化的写作风格。鉴于需要一种能够从社交媒体文本中有效提取有用模式的最优语言模型，本文的关键目标是训练语言模型，使其学会推导出通用模式。通过引入随机权重扰动和对比学习策略来实现这一目标。在独特的训练策略基础上，提出了一种元预测器，它利用5种不同语言模型的优势，将社交媒体文本帖子区分为非健康类和健康类。在三个公开基准数据集上的全面实验表明，与传统训练相比，所提出的训练策略使语言模型的性能在F1分数上提升了高达3.87%。此外，所提出的元预测器在所有三个基准数据集上的健康提及分类性能均优于现有方法。