UC Irvine

Introduction: Online mental health communities offer people experiencing mental health struggles a valuable source of social support. This project uses computational methods to explore the collective wisdom of thousands of archived support interactions from these spaces. Methods: Using a corpus of 12,325 responses to 7,646 questions from the online support forums r/Anxiety and r/socialanxiety and crowdsourced response quality ratings for 790 of the responses, we successfully trained a random forest classifier (AUC= 0.82) to label responses to anxiety-related questions as high- or low-quality. We applied this classifier to the full dataset to conduct several quantitative and exploratory analyses. Results: Response length was the strongest predictor of response quality among 31 metadata and linguistic features of responses. Both emotional support (ρ= 0.47, p< 0.001) and informational support (ρ= 0.62, p< 0.001) were positively correlated with response quality. Sentiment differed slightly across questions and responses as well as across subreddits. Common bigrams in high-quality responses seemed to be more positive than common bigrams in low-quality responses. Conclusions: This work provides insight into the content and form of supportive interactions in online mental health communities, potentially informing how these spaces are designed and moderated. Our findings can also support the development of artificial intelligence question-answering systems for mental health.