一种机器学习方法来检测广播媒体中潜在有害和保护性自杀相关的内容
Hannah Metzler1,2,3,4, Hubert Baginski3,5, David Garcia1,3,6,7
1Section for Science of Complex Systems, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.
PloS one
|May 14, 2024
概括
机器学习有效地分类与自杀相关的广播媒体内容. 模型准确地识别内容特征,通过大规模的媒体选和分析来帮助预防自杀的努力.
科学领域:
- 计算语言学 计算语言学
- 媒体研究 媒体研究
- 公共卫生 公共卫生
背景情况:
- 媒体报道自杀事件可能会产生有害或预防性影响.
- 自杀内容的广播媒体的自动选受到机器学习 (ML) 工具缺乏的限制.
- 现有的ML方法难以检测新闻报道中的特定特征.
研究的目的:
- 应用ML标签广播媒体数据用于预防自杀.
- 开发和评估ML模型,用于在电视和广播中分类与自杀相关的内容.
- 在各种分类任务和内容特征中评估模型性能.
主要方法:
- 来自44个来源的2519个英语广播成绩单的手动标签 (2019年4月至2020年3月).
- 媒体报道的内容分析,以特定特征进行分析.
- 培训和基准测试ML模型:多数分类器,TF-IDF与线性SVM,以及BERT.
- 模型应用于简单和复杂的分类任务,包括从14个类别中确定主要焦点.
主要成果:
- 使用SVM和BERT模型的TF-IDF的表现优于多数分类器.
- 在测试数据集中,F1分数从0.90 (名人自杀) 到0.58 (主要焦点识别) 之间.
- 模型的性能主要取决于训练样本的数量,而不是任务难度.
结论:
- 机器学习模型可以在与自杀相关的广播媒体内容的分类方面取得令人满意的结果,包括多类特征.
- 足够的培训数据对于高模型性能至关重要.
- 开发的模型有助于对广播媒体进行大规模的选和调查,以预防自杀.
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