社交媒体上的自杀风险评估与半监督学习
Max Lovitt1,2, Haotian Ma1, Song Wang3
1Population Health Sciences, Weill Cornell Medicine, New York, New York, USA.
Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data
|February 3, 2025
概括
本研究引入了一种半监督学习框架,以改进来自社交媒体帖子的自动自杀风险评估,解决数据限制. 这种新的方法通过使用伪标记数据来提高模型性能,有助于早期检测.
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
- 机器学习用于社会利益.
背景情况:
- 社交媒体平台越来越多地被表达自杀想法的人使用.
- 使用自然语言处理 (NLP) 进行自动自杀风险评估是有希望的,但由于标记数据不足和阶级不平衡而受到阻碍.
- 现有的方法与现实世界社交媒体数据的固有挑战作斗争,以检测自杀风险.
研究的目的:
- 开发一个强大的半监督学习框架,用于自动自杀风险评估.
- 解决自杀相关社交媒体内容中有限的标签数据和阶级不平衡的挑战.
- 提高自杀风险检测模型的准确性和可靠性.
主要方法:
- 提出了一个半监督的框架,将500个标记和1500个未标记的社交媒体帖子结合起来.
- 扩展了自我训练算法,采用了一种针对不平衡数据集的新型伪标签获取过程.
- 实施伪标签数据子集的手动验证,以确保质量和可靠性.
- 评估了多个模型,确定RoBERTa是框架的最佳支柱.
主要成果:
- 半监督框架显著提高了模型评估自杀风险的能力.
- 这种新的伪标签策略有效地处理了阶级不平衡问题.
- 部分验证的伪标签数据,结合基准真相标签,增强了预测性能.
- 作为风险评估的基础模型,RoBERTa表现出卓越的表现.
结论:
- 半监督学习,特别是精细的伪标签过程,对于社交媒体上的自杀风险评估是有效的.
- 拟议的框架为这一关键领域的数据稀缺性和不平衡问题提供了可行的解决方案.
- 这种方法有可能开发出更准确,更可扩展的自动化系统来预防自杀.
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