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An ever-increasing amount of social media content requires advanced AI-basedcomputer programs capable of extracting useful information. Specifically, theextraction of health-related content from social media is useful for thedevelopment of diverse types of applications including disease spread,mortality rate prediction, and finding the impact of diverse types of drugs ondiverse types of diseases. Language models are competent in extracting thesyntactic and semantics of text. However, they face a hard time extractingsimilar patterns from social media texts. The primary reason for this shortfalllies in the non-standardized writing style commonly employed by social mediausers. Following the need for an optimal language model competent in extractinguseful patterns from social media text, the key goal of this paper is to trainlanguage models in such a way that they learn to derive generalized patterns.The key goal is achieved through the incorporation of random weightedperturbation and contrastive learning strategies. On top of a unique trainingstrategy, a meta predictor is proposed that reaps the benefits of 5 differentlanguage models for discriminating posts of social media text into non-healthand health-related classes. Comprehensive experimentation across 3 publicbenchmark datasets reveals that the proposed training strategy improves theperformance of the language models up to 3.87%, in terms of F1-score, ascompared to their performance with traditional training. Furthermore, theproposed meta predictor outperforms existing health mention classificationpredictors across all 3 benchmark datasets.

A Unique Training Strategy to Enhance Language Models Capabilities for Health Mention Detection from Social Media Content