Loneliness is an enduring global health issue. Effective evidence-based loneliness interventions exist but lack sufficient scale to reach many who might benefit from them. Single-session interventions (SSIs), which aim to condense the core elements of evidence-based treatments into brief and broadly acceptable self-guided experiences, offer an opportunity to make robust guidance for overcoming loneliness much more accessible. This dissertation’s objective was to inform the development of effective and broadly-appealing SSIs for loneliness. Study 1 (n = 908) showed that neither a 20-minute SSI version (p = 0.22) nor a 1-hour 3-session version of a self-guided online loneliness intervention (p = 0.23) changed loneliness over four weeks more than an active control SSI did. However, participants reported finding both loneliness interventions more appealing and valuable than the control (ps < 0.02). While some participants reported that the loneliness interventions had a lasting positive impact, the interventions did not appear to be more useful on average than an active control among lonely people aged 16 and older with internet access. Study 2 explored if popular online content was more effective at reducing distress than typical researcher-created SSI content among a sample of mostly crowdworkers struggling with psychological distress. The study (n = 916) showed that a popular online content-centered mental health SSI did not affect participants’ distress over four weeks differently than a mental health SSI with researcher-created content (p = 0.09) or finding content independently on the web (p = 0.42). On the contrary, participants assigned to the researcher-created SSI reported greater improvement than those assigned to the popular online content-based SSI in depressive symptoms (b = -0.44, p = 0.03) and loneliness (b = 0.29, p = 0.04), on average. Future work should aim to improve SSIs’ effectiveness, identify populations for which SSIs are most effective, and implement SSIs in accessible and appealing ways.

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.