Augmenting Reddit Posts to Determine Wellness Dimensions impacting
  Mental Health
Chandreen Liyanage Muskan Garg Vijay Mago Sunghwan Sohn
Amid ongoing health crisis, there is a growing necessity to discern possible
signs of Wellness Dimensions (WD) manifested in self-narrated text. As the
distribution of WD on social media data is intrinsically imbalanced, we
experiment the generative NLP models for data augmentation to enable further
improvement in the pre-screening task of classifying WD. To this end, we
propose a simple yet effective data augmentation approach through prompt-based
Generative NLP models, and evaluate the ROUGE scores and syntactic/semantic
similarity among existing interpretations and augmented data. Our approach with
ChatGPT model surpasses all the other methods and achieves improvement over
baselines such as Easy-Data Augmentation and Backtranslation. Introducing data
augmentation to generate more training samples and balanced dataset, results in
the improved F-score and the Matthew's Correlation Coefficient for upto 13.11%
and 15.95%, respectively.