Reasoning Under Distress: Mining Claims and Evidence in Mental Health Narratives

This paper explores the application of argument mining to mental health narratives using zero-shot transfer learning.We fine-tune a BERT-based sentence classifier on 15k essays from the Persuade dataset-achieving 69.1% macro-F1 on its test set-and apply it without domain adaptation to the CAMS dataset, which consists of anonymized mental health-related Reddit posts.On a manually annotated gold-standard set of 150 CAMS sentences, our model attains 54.7% accuracy and 48.9% macro-F1, with evidence detection (F1 = 63.4%)transferring more effectively than claim identification (F1 = 32.0%).Analysis across expert-annotated causal factors of distress shows that personal narratives heavily favor experiential evidence (65-77% of sentences) compared to academic writing.The prevalence of evidence sentences, many of which appear to be grounded in lived experiences, such as descriptions of emotional states or personal events, suggests that personal narratives favor descriptive recollection over formal, argumentative reasoning.These findings underscore the unique challenges of argument mining in affective contexts and offer recommendations for enhancing argument mining tools within clinical and digital mental health support systems.