Understanding mental health discourse on Reddit with transformers and explainability

Area of Science:

• Computational linguistics
• Mental health informatics
• Social media analysis

Background:

• Social media platforms host significant discussions on mental health.
• Understanding online discourse is crucial for mental health support and research.
• Specialized subreddits aim to provide focused support for various mental health conditions.

Purpose of the Study:

• To classify Reddit posts related to anxiety, depression, bipolar disorder, and borderline personality disorder (BPD).
• To assess the alignment of subreddit conversations with their intended mental health focus.
• To identify linguistic patterns characterizing discussions of specific mental health conditions.

Main Methods:

• Fine-tuned pretrained transformer models (BERT and MentalBERT) were employed for text classification.
• Local Interpretable Model-agnostic Explanations (LIME) were used for model interpretability.
• Analysis focused on posts from specialized subreddits dedicated to mental health conditions.

Main Results:

• Classification models achieved an average accuracy of 82%, with MentalBERT showing slightly superior performance.
• Distinct linguistic patterns were identified for different conditions (e.g., mood instability in bipolar disorder, emotional regulation in BPD).
• The study demonstrated the effectiveness of AI in analyzing and understanding online mental health discourse.

Conclusions:

• AI models can effectively classify mental health topics on social media and assess community alignment.
• Identifying specific language use provides insights into how different conditions are discussed online.
• These findings can inform mental health professionals, community management, and efforts to reduce stigma.