Mental-health stigma remains a pervasive social problem that hampers treatment-seeking and recovery. Existing resources for training neural models to finely classify such stigma are limited, relying primarily on social-media or synthetic data without theoretical underpinnings. To remedy this gap, we present an expert-annotated, theory-informed corpus of human-chatbot interviews, comprising 4,141 snippets from 684 participants with documented socio-cultural backgrounds. Our experiments benchmark state-of-the-art neural models and empirically unpack the challenges of stigma detection. This dataset can facilitate research on computationally detecting, neutralizing, and counteracting mental-health stigma. Our corpus is openly available at https://github.com/HanMeng2004/Mental-Health-Stigma-Interview-Corpus.

In this short paper, we compare existing value systems and approaches in NLP and HCI for collecting narrative data. Building on these parallel discussions, we shed light on the challenges facing some popular NLP dataset types, which we discuss these in relation to widely-used narrative-based HCI research methods; and we highlight points where NLP methods can broaden qualitative narrative studies. In particular, we point towards contextuality, positionality, dataset size, and open research design as central points of difference and windows for collaboration when studying narratives. Through the use case of narratives, this work contributes to a larger conversation regarding the possibilities for bridging NLP and HCI through speculative mixed-methods.