用聊天机器人+AI分析，揭秘抑郁症污名的心理机制
Deconstructing Depression Stigma: Integrating AI-driven Data Collection and Analysis with Causal Knowledge Graphs
- 通过对话机器人收集1002人访谈数据，结合AI辅助编码
- 发现个体态度模式及心理构念间的因果关联
- 为数字干预和心理健康包容性设计提供新思路
抑郁症污名是长期存在的社会问题，阻碍治疗与康复。现有研究需大量人力分析相关数据，为此我们设计了一款聊天机器人，与1002名参与者展开对话，借助人工智能辅助进行定性编码，并基于编码结果构建因果知识图谱以解析污名成因。结果显示，该聊天机器人能有效获取人们对抑郁症的态度信息，且AI辅助编码与人工专家编码高度一致。本研究融合大语言模型（LLMs）与因果知识图谱，揭示了个体回应中的模式以及数据整体中心理构念的相互关系。论文还探讨了这些发现对人机交互（HCI）研究者在开发数字干预、分解人类心理构念及促进包容性态度方面的启示。
原文摘要 · Abstract (English)
Mental-illness stigma is a persistent social problem, hampering both treatment-seeking and recovery. Accordingly, there is a pressing need to understand it more clearly, but analyzing the relevant data is highly labor-intensive. Therefore, we designed a chatbot to engage participants in conversations; coded those conversations qualitatively with AI assistance; and, based on those coding results, built causal knowledge graphs to decode stigma. The results we obtained from 1,002 participants demonstrate that conversation with our chatbot can elicit rich information about people's attitudes toward depression, while our AI-assisted coding was strongly consistent with human-expert coding. Our novel approach combining large language models (LLMs) and causal knowledge graphs uncovered patterns in individual responses and illustrated the interrelationships of psychological constructs in the dataset as a whole. The paper also discusses these findings' implications for HCI researchers in developing digital interventions, decomposing human psychological constructs, and fostering inclusive attitudes.
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