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从关键杆机器学习到检测青年在社交媒体上的自伤和自杀风险:算法开发和验证研究.

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  • 1Department of Computer Science, Vanderbilt University, Nashville, TN, United States.

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在私人Instagram消息中检测青少年自我伤害或自杀 (SH-S) 想法需要细微的,具有背景意识的模型. 扩大对话上下文显著提高了识别SH-S表达式的准确性,突出了它对心理健康工具的重要性.

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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
  • 青少年心理学 青少年心理学

背景情况:

  • 青少年 (13-21岁) 的私人Instagram对话被分析为自我伤害或自杀 (SH-S) 的想法.
  • 现有的自动心理健康工具需要改进,以识别与SH-S相关的细微年轻语言.

研究的目的:

  • 开发可解释的机器学习模型,以检测青年对话中的SH-S表达式的频谱.
  • 超越简单的二进制分类,了解各种SH-S语言.

主要方法:

  • 通过使用传统和基于变压器的机器学习模型,分析了青年捐赠的Instagram私人对话.
  • 嵌入的特征:心理语言,情感,词汇和对话上下文 (消息到子对话层面).
  • 评估模型包括来自变压器的双向编码器表示和来自变压器的蒸双向编码器表示.

主要成果:

  • 来自变压器的蒸双向编码器表示实现了99%的准确性,用于单个消息中的SH-S存在.
  • 细粒度分类 (自我,其他,夸张) 的准确率为89%,随着扩展对话背景,提高到91%.
  • 对上下文的理解对于区分微妙的SH-S话语变异至关重要.

结论:

  • SH-S自动检测系统必须对社交媒体上的动态青年语言敏感.
  • 情境和情绪意识模型增强了对SH-S风险的检测和细微理解.
  • 研究为伦理干预提供了基础,需要跨平台和跨人群验证.