在Twitter上使用#ActuallyAutistic用于精确诊断自闭症谱系障碍:机器学习研究
Aditi Jaiswal1, Peter Washington1
1Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States.
JMIR formative research
|February 14, 2024
概括
这项研究使用了Twitter数据来识别自闭症谱系障碍 (ASD) 患者的语言模式,在分类推文方面达到73%的准确性,在分类用户个人资料方面达到80.5%. 研究结果表明,社交媒体的文字可以帮助自闭症研究.
科学领域:
- 计算语言学计算语言学
- 神经科学是一个神经科学.
- 公共卫生信息学 公共卫生信息学
背景情况:
- 社交媒体平台产生了大量用户生成的内容,提供了对公共卫生趋势的见解.
- 推特 (X) 数据为了解公共卫生,包括自闭症谱系障碍 (ASD) 提供了宝贵的资源.
- 这项研究开创了Twitter数据挖掘用于自闭症查和分析的应用.
研究的目的:
- 通过使用Twitter数据,分析那些与ASD认同的人的行为特征和情绪表达.
- 通过检查ASD个人的数字足迹中的语言模式来增强ASD分析研究.
- 通过先进的数据挖掘技术,提高自闭症查的准确性.
主要方法:
- 开发了一种机器学习模型,以根据Twitter文本来区分ASD个体与神经典型个体.
- 收集了来自自认自闭症的用户 (#ActuallyAutistic) 和对照组的6,515,470条推文.
- 利用word2vec和基于注意力的双向长期短期记忆模型进行推特和用户配置文件分类.
主要成果:
- 推特分类器实现了73%的准确性,AUC为0.728和F1得分为0.71.
- 用户配置文件分类器表现出强的性能,AUC为0.78和F1得分为0.805.
- 这些结果凸显了数字表型化在使用社交媒体文本数据进行大规模干预方面的潜力.
结论:
- 社交媒体沟通中的语言变化可以在自然环境中促进症状学研究.
- 社交媒体数据挖掘为自闭症研究和临床见解提供了一种新的方法.
- 这项研究强调了分析在线沟通模式的有用性,以了解ASD特征.
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