Student loan borrowers often face mental health challenges, including anxiety and stress, but struggle to find adequate support due to high treatment costs, limited access, and stigma. As a result, many turn to online communities for emotional support. We examine how student loan borrowers express different emotions regarding their debt in an online community, r/personalfinance on Reddit. Specifically, we dived deeper into users’ posts that were labeled “neutral” through an initial machine learning (ML) modeling. We applied a transformer-based emotion detection ML model and keyword extraction methods to uncover hidden emotions in posts marked as “neutral” previously. Our dataset consisted of over 225,000 posts, filtered down to 67,000 “neutral” posts. We found that users expressed frustration with repayment challenges, retirement-related stress, and advice-seeking behavior. This study contributes to the growing body of literature on mental health concerns related to student debt by enhancing emotion detection models to improve NLP tools for consumer research. Additionally, it also reveals the hidden stressors faced by borrowers, and demonstrates the value of social media in monitoring public well-being – offering insights for consumer wellbeing and mental health professionals.

Accepted Poster Presentation