通过使用多维文本特征在在线社交媒体平台上检测精神疾病的有效方法
Faheem Ahmad Wagay1, Jahiruddin1
1Department of Computer Science, Jamia Millia Islamia University, Jamia Nagar, New Delhi 110025, India.
Acta psychologica
|January 28, 2026
概括
这项研究通过整合多个文本功能来增强社交媒体上的精神疾病检测. 这种新型模型在各种数据集中显示出更好的准确性和适应性,以更好地在线监测心理健康.
科学领域:
- 计算语言学计算语言学
- 数字心理健康数字心理健康
- 机器学习用于社会利益.
背景情况:
- 在线社交媒体是精神疾病检测研究的关键领域.
- 目前使用单一文本特征的方法在捕捉内容细微差别方面受到限制.
- 开发先进的检测模型对于心理健康监测至关重要.
研究的目的:
- 为了改善社交媒体帖子中的精神疾病检测.
- 引入一个整合多个文本信息维度的模型.
- 为了提高不同数据集的分类性能.
主要方法:
- 开发了一种结合词语嵌入,情感,情感和主题上下文的新型模型.
- 绩效是根据最先进的基线进行评估的.
- 利用了两个现实世界的Reddit数据集,其大小和类分布各不相同.
主要成果:
- 拟议的模型在精度,回忆,MCC和F1分数方面表现出卓越的表现.
- 在较大的数据集上达到75.13%的准确性,在较小/多样化的数据集上达到69.49%的准确性.
- 结果强调了模型在不同数据条件下的稳定性和适应性.
结论:
- 整合多维文本特征显著提高了心理疾病检测的准确性和概括性.
- 这种方法为未来的社交媒体心理健康研究提供了有希望的方向.
- 允许在社交平台上进行心理健康监测的实际应用.
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