根据经验采样方法,数字表型和社交网络测量的社会背景来预测情绪
Anna M Langener1,2,3, Laura F Bringmann4,5, Martien J Kas6
1Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands. a.m.langener@rug.nl.
Administration and policy in mental health
|January 10, 2024
概括
使用数字表型,体验采样方法 (ESM) 和自我中心网络从社会背景中预测情绪的准确性较低. 结合数据源略有改善了预测,但在临床使用中需要对多样样样本进行进一步研究.
科学领域:
- 心理学 心理学 心理学
- 计算社会科学 计算社会科学
- 数字健康数字健康
背景情况:
- 社交互动对幸福和情绪至关重要.
- 准确地捕捉一个人的社会背景是预测福祉的关键.
- 现有的方法,如数字表型,ESM和自我中心的网络测量不同的社会方面.
研究的目的:
- 研究基于社会背景的情绪预测准确度.
- 评估结合多个数据源用于情绪预测的实用性.
- 解决研究中的差距,结合各种社会背景测量方法.
主要方法:
- 在28天内从学生样本 (N=11) 收集了密集的个人数据.
- 使用数字表型,经验采样方法 (ESM) 和自我中心网络.
- 训练有素的个性化随机森林机器学习模型,在各种时间尺度上总结了预测因素.
主要成果:
- 即使将不同方法的社交互动数据结合起来,情绪预测的准确性仍然很低 (R2=0.06对于影响).
- 预测准确性在个体之间有很大差异,最佳预测因素不同.
- 对大多数参与者来说,结合预测因素通常可以改善情绪预测.
结论:
- 整合多样化的社会背景数据极小地提高了情绪预测的准确性.
- 需要对更大,更多样化的样本进行进一步的研究,以提高临床效用.
- 目前对情绪的预测建模方法需要改进.
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