解构抑郁症污名：整合AI驱动的数据收集与分析与因果知识图谱
Deconstructing Depression Stigma: Integrating AI-driven Data Collection and Analysis with Causal Knowledge Graphs
AI总结：
针对精神疾病污名阻碍治疗与康复且相关数据分析劳动强度高的问题，本文设计聊天机器人收集对话，结合AI辅助编码与因果知识图谱解码污名，揭示态度模式与心理构念关系，为HCI研究提供干预等方向的启示。
摘要：
精神疾病污名是一个长期存在的社会问题，阻碍了寻求治疗和康复。因此，迫切需要更清晰地理解它，但分析相关数据的劳动强度极高。为此，我们设计了一个聊天机器人与参与者进行对话；在AI辅助下对这些对话进行定性编码；并基于编码结果构建因果知识图谱来解码污名。从1002名参与者中获得的结果表明，与我们的聊天机器人对话可获取人们对抑郁症态度的丰富信息，而AI辅助编码与人类专家编码高度一致。我们结合大型语言模型（LLMs）和因果知识图谱的新方法揭示了个体反应的模式，并阐明了数据集中心理构念的整体相互关系。本文还讨论了这些发现对HCI研究者在开发数字干预、分解人类心理构念和培养包容态度方面的意义。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
