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.

Text simplification is essential for making public health information accessible to diverse populations, including those with limited health literacy. However, commonly used evaluation metrics in Natural Language Processing (NLP)—such as BLEU, FKGL, and SARI—mainly capture surface-level features and fail to account for human-centered qualities like clarity, trustworthiness, tone, cultural relevance, and actionability. This limitation is particularly critical in high-stakes health contexts, where communication must be not only simple but also usable, respectful, and trustworthy. To address this gap, we propose the Human-Centered Readability Score (HCRS), a five-dimensional evaluation framework grounded in Human-Computer Interaction (HCI) and health communication research. HCRS integrates automatic measures with structured human feedback to capture the relational and contextual aspects of readability. We outline the framework, discuss its integration into participatory evaluation workflows, and present a protocol for empirical validation. This work aims to advance the evaluation of health text simplification beyond surface metrics, enabling NLP systems that align more closely with diverse users’ needs, expectations, and lived experiences.

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.