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利用社交媒体预测COVID-19引起的大学生心理健康的障碍:建模研究研究
Vedant Das Swain1, Jingjing Ye2, Siva Karthik Ramesh2
1Khoury College of Computer Sciences, Northeastern University, Boston, MA, United States.
JMIR formative research
|June 25, 2024
概括
分析私人社交媒体语言的机器学习模型准确地预测了COVID-19期间大学生的心理健康障碍,超过了传统调查.
科学领域:
- 计算社会科学 计算社会科学
- 心理健康信息学心理健康信息学
- 机器学习应用程序 机器学习应用程序
背景情况:
- 像COVID-19这样的大规模危机显著影响学生的心理健康.
- 传统的调查不足以及时进行大规模的心理健康监测.
- 社交媒体数据为识别有风险的学生提供了一个可扩展的,客观的替代方案.
研究的目的:
- 评估使用私人社交媒体语言来检测危机引起的精神健康障碍的可行性.
- 开发和验证用于预测学生心理健康变化的机器学习模型.
主要方法:
- 在两年的时间里,分析了43名本科生在Facebook上的4124条帖子.
- 从用户帖子和评论中提取心理语言学趋势.
- 基于这些趋势,开发机器学习模型来预测心理健康障碍.
主要成果:
- 社交媒体模型获得了0.79的F1得分,比自我报告的数据有39%的改善.
- 功能预测了焦虑,抑郁,社会隔离和自杀行为,F1分数从0.85到0.93.
- 最佳预测发生在封锁后的7个月,这表明数据最小化的潜力.
结论:
- 使用私人社交媒体语言的机器学习模型有效地预测了学生中COVID-19相关的心理健康障碍.
- 在线行为中的纵向心理语言趋势是心理健康变化的强有力的预测因素.
- 这种方法有可能在危机期间提供早期,自动化的心理健康警告.
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