Augmenting Reddit Posts to Determine Wellness Dimensions impacting Mental Health

Area of Science:

• Natural Language Processing
• Computational Social Science
• Digital Health

Background:

• Growing need to identify Wellness Dimensions (WD) in self-narrated text during health crises.
• Social media data presents imbalanced distributions of WD, hindering accurate classification.
• Existing data augmentation methods may not sufficiently address the nuances of WD in text.

Purpose of the Study:

• To investigate the efficacy of generative Natural Language Processing (NLP) models for data augmentation in classifying Wellness Dimensions (WD).
• To improve the pre-screening task for WD detection using augmented datasets.
• To evaluate a prompt-based generative NLP approach against established augmentation techniques.

Main Methods:

• Experimented with prompt-based generative NLP models, including ChatGPT, for data augmentation.
• Compared augmented data with existing interpretations using ROUGE scores and syntactic/semantic similarity.
• Evaluated performance against baseline methods like Easy-Data Augmentation and Backtranslation.

Main Results:

• The ChatGPT model-based data augmentation approach outperformed other methods.
• Significant improvements observed in F-score (up to 13.11%) and Matthew's Correlation Coefficient (up to 15.95%).
• Generated augmented data demonstrated high syntactic and semantic similarity to existing interpretations.

Conclusions:

• Prompt-based generative NLP models offer an effective strategy for augmenting imbalanced datasets for WD classification.
• Data augmentation using advanced NLP techniques can substantially enhance the accuracy of pre-screening tasks.
• This approach holds promise for improving the analysis of digital health indicators in social media.