在Twitter上识别自我披露的焦虑:一种自然语言处理方法
Daniel Zarate1, Michelle Ball2, Maria Prokofieva2
1College of Health and Biomedicine, Royal Melbourne Institute of Technology (RMIT), Australia.
Psychiatry research
|November 13, 2023
概括
社交媒体文本分析揭示了焦虑的不同语言模式. 机器学习可以根据Twitter帖子准确预测焦虑诊断,突出消极情绪和减少发布频率.
科学领域:
- 计算语言学计算语言学
- 心理健康信息学心理健康信息学
- 社交媒体分析.
背景情况:
- 社交媒体平台为心理健康研究提供了有价值的数据.
- 对社交媒体帖子的文字分析可以识别与心理状况相关的模式.
- 这项研究的重点是了解Twitter上焦虑的数字足迹.
研究的目的:
- 在Twitter上探索与自我报告的焦虑诊断相关的不同语言模式.
- 调查语言特征在识别焦虑中的预测能力.
- 分析焦虑症患者的用户行为和语言特征.
主要方法:
- 对来自605名用户的233,000条推特进行了比较分析 (焦虑者与非焦虑者).
- 利用自然语言处理,包括语言调查词数 (LIWC) 和情感分析.
- 员工监督的机器学习模型和隐藏的个人资料分析 (LPA).
主要成果:
- 机器学习模型在预测焦虑诊断方面取得了高准确率 (高达81.1%) .
- 确定了四个不同的用户配置文件:高情绪,低情绪,自我沉浸和规范.
- 焦虑的用户表现出高负面情绪,低积极情绪,减少发帖频率,以及更长的短信.
结论:
- 推特帖子中明显的语言模式与自我报告的焦虑相关.
- 这些模式可以准确预测焦虑的诊断.
- 调查结果支持在线心理健康活动和宣传倡议的发展.
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