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.

Social media are a critical component of the information ecosystem during public health crises. Understanding the public discourse is essential for effective communication and misinformation mitigation. Computational methods can aid these efforts through online social listening. We combined hierarchical text clustering and sentiment analysis to examine the face mask-wearing discourse in Germany during the COVID-19 pandemic using a dataset of 353,420 German X (formerly Twitter) posts from 2020. For sentiment analysis, we annotated a subsample of the data to train a neural network for classifying the sentiments of posts (neutral, negative, or positive). In combination with clustering, this approach uncovered sentiment patterns of different topics and their subtopics, reflecting the online public response to mask mandates in Germany. We show that our approach can be used to examine long-term narratives and sentiment dynamics and to identify specific topics that explain peaks of interest in the social media discourse.