Social media is a powerful tool also for discussing mental health. The conversations that take place in these spaces provide a unique insight into how users talk about the issue. This study uses fine-tuned pretrained transformer models (BERT and MentalBERT), to classify Reddit posts about anxiety, depression, bipolar disorder and borderline personality disorder (BPD) in specialised subreddits. By assessing how well subreddit conversations align with their intended mental health focus, the analysis ensures that these communities are effectively serving their purpose as support spaces. Our classification models achieve an average accuracy of 82%, with MentalBERT slightly outperforming BERT. To ensure transparency, we use Local Interpretable Model-agnostic Explanations (LIME) to identify key linguistic patterns that influence the model predictions. The outcome reveals distinct language use across conditions: as examples, discussions in bipolar disorder subreddits often refer to mood instability, while BPD communities emphasise challenges in emotional regulation. By integrating classification with explainability, this study offers insights into thematic patterns in online discourse that can support mental health professionals in identifying trends. While our models are not diagnostic tools, they function as subreddit-alignment classifiers, helping to uncover how different topics are discussed across communities. These insights may inform human-in-the-loop community management strategies and contribute to raising awareness and reducing stigma around mental health issues, ultimately fostering more supportive digital environments.
Rodríguez et al. (Sat,) studied this question.