Han Meng

Peinuan Qin, Jingzhu Chen, Yitian Yang et al.

Conversational interviews are commonly used to complement structured surveys by eliciting rich and contextualized responses, which are typically analyzed qualitatively. However, their potential contribution to quantitative measurement remains underexplored. In this paper, we introduce ConvScale, an AI-supported approach that transforms psychometric scales into natural conversational interviews while preserving the original measurement structure. Based on interview data, ConvScale predicts item-level scores and aggregates them to derive scale-based assessments. In a within-subjects study with 18 participants, our results show that ConvScale-derived scores align closely with participants' self-report scores at both the item and construct levels, while maintaining moderate internal reliability; however, the structural validity was inadequate. In light of this, we discussed the potential of supporting quantitative measurement through interviews and proposed implications for future designs.

Han Meng, Yancan Chen, Yunan Li et al.

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.

Han Meng, Yitian Yang, Yunan Li et al.

Qualitative analysis is a challenging, yet crucial aspect of advancing research in the field of Human-Computer Interaction (HCI). Recent studies show that large language models (LLMs) can perform qualitative coding within existing schemes, but their potential for collaborative human-LLM discovery and new insight generation in qualitative analysis is still underexplored. To bridge this gap and advance qualitative analysis by harnessing the power of LLMs, we propose CHALET, a novel methodology that leverages the human-LLM collaboration paradigm to facilitate conceptualization and empower qualitative research. The CHALET approach involves LLM-supported data collection, performing both human and LLM deductive coding to identify disagreements, and performing collaborative inductive coding on these disagreement cases to derive new conceptual insights. We validated the effectiveness of CHALET through its application to the attribution model of mental-illness stigma, uncovering implicit stigmatization themes on cognitive, emotional and behavioral dimensions. We discuss the implications for future research, methodology, and the transdisciplinary opportunities CHALET presents for the HCI community and beyond.