雷格与Reddit:用LLM和主题建模解码Reddit上的自闭症对话
Deshan Wattegama1, Benjamin Black2,3, Marcus Moen4
1Institute for Data Science and Informatics, University of Missouri.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
在Reddit上的在线自闭症社区提供实际支持和非传统治疗见解,与临床建议不同. 一个针对自闭症的提取增强代 (RAG) 系统显示了改善自闭症指导的潜力.
科学领域:
- 神经发育障碍 神经发育障碍
- 医疗保健中的人工智能
- 社交计算社会计算
背景情况:
- 像Reddit这样的社交媒体平台对于寻求信息和支持的自闭症患者和护理人员至关重要.
- 大型语言模型 (LLM) 越来越多地用于医疗咨询,需要对其在自闭症护理中的作用进行评估.
- 了解在线与临床讨论的细微差别对于全面的自闭症支持至关重要.
研究的目的:
- 分析Reddit上的与自闭症相关的讨论,并将其与临床医生和患者之间的互动进行比较.
- 评估自闭症特异性回收增强生成 (RAG) 系统在提供指导方面的有效性.
- 确定在线社区与临床环境中讨论的支持策略中的关键主题和差异.
主要方法:
- 使用BERTopic建模来识别来自r/autism和r/autism_parenting的Reddit讨论中的关键主题.
- 将在线讨论的主题与密苏里大学普森自闭症和神经发育中心的临床信息进行了比较.
- 评估大型语言模型 (LLM) 产生的响应与Reddit的同行建议对比,评估准确性,相关性,同理心和帮助力.
主要成果:
- 在Reddit的讨论中,行为挑战和实际支持策略占据着突出的位置.
- 在线社区强调非传统疗法,与临床环境对药物管理的关注形成鲜明对比.
- 在LLM回复和同行建议之间观察到准确性,相关性,同理心和帮助度的差异.
结论:
- 与自闭症相关的在线讨论提供了宝贵的见解,特别是关于非传统的支持和实际建议.
- 检索增强代 (RAG) 系统有望通过整合社区驱动的信息来增强自闭症指导.
- 一种平衡的方法,包括社区智慧和临床专业知识,对于有效的自闭症护理至关重要.
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