构建心理疾病污名语料库，助力算法识别与消除偏见
What is Stigma Attributed to? A Theory-Grounded, Expert-Annotated Interview Corpus for Demystifying Mental-Health Stigma
- 基于理论框架的人机对话访谈数据集，含4141个片段
- 684名参与者具社会文化背景记录，提升标注可靠性
- 公开数据集适合研究污名检测与干预策略
心理疾病污名仍是阻碍治疗与康复的普遍社会问题。现有用于训练神经模型细粒度分类污名的资源有限，主要依赖社交媒体或合成数据，缺乏理论基础。为弥补这一缺口，我们构建了一个由专家标注、理论指导的人机对话访谈语料库，包含来自684名参与者的4141个片段，且每位参与者均有明确的社会文化背景信息。实验对当前最先进的神经模型进行了基准测试，并实证揭示了污名检测面临的挑战。该数据集可推动计算方法在心理疾病污名检测、中和与对抗方面的研究。语料库已开源，地址为 https://github.com/HanMeng2004/Mental-Health-Stigma-Interview-Corpus。
原文摘要 · Abstract (English)
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.
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