使用大型语言模型监测残疾人的社交媒体:对推特中的情绪和残疾模型的分析
Abdul Hamid Dabboussi1, Iman Yousuf2, Hannah Bullock3
1Lassonde School of Engineering, York University, Toronto, Canada.
Studies in health technology and informatics
|May 17, 2025
概括
大型语言模型在分析在线残疾人权利对话方面表现有前途. 他们准确地分类推特情绪,并区分残疾的社会和医疗模型.
科学领域:
- 人工智能的人工智能
- 计算社会科学 计算社会科学
- 关于残疾人的研究.
背景情况:
- 在线话语显著塑造了公众对残疾人权利的看法.
- 大型语言模型 (LLM) 提供了分析大规模数字对话的潜力.
- 了解残疾的框架 (社会与医疗模式) 对于倡导至关重要.
研究的目的:
- 评估LLM在分析有关残疾人权利的推文中的有效性.
- 确定LLM在残疾相关内容的情感分析方面的能力.
- 评估LLM在区分残疾的社会和医疗模型的表现.
主要方法:
- 收集了5000条与残疾人权利相关的推文.
- 训练一个用于内容分析的大型语言模型.
- 对情绪和模型分类的模型准确性的定量评估.
主要成果:
- 经过培训的LLM在识别推特情绪 (积极/消极) 中表现出高度准确性.
- 该模型在根据残疾的社会与医疗模型对推特进行分类时取得了有希望的准确性.
- 结果表明,LLM是分析残疾在线讨论细微差别的有效工具.
结论:
- 法律残疾管理工具是对在线关于残疾人权利话语的自动化分析的可行工具.
- 这项技术可以帮助研究人员和倡导者了解公众论和对残疾的看法.
- 进一步的研究可以为更复杂的社会科学调查改进LLM应用.
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