论文标题
使用深度学习对自杀构想检测进行定量和定性分析
A Quantitative and Qualitative Analysis of Suicide Ideation Detection using Deep Learning
论文作者
论文摘要
为了防止青年自杀，社交媒体平台受到了研究人员的广泛关注。一些研究应用机器学习或基于深度学习的文本分类方法来对包含自杀风险的社交媒体帖子进行分类。本文复制了基于社交媒体的竞争性自杀性检测/预测模型。我们评估了使用多个数据集和不同最先进的深度学习模型（RNN-，CNN-和基于注意力的模型）检测自杀构想的可行性。使用两个自杀性评估数据集，我们通过定量和定性方式评估了4种输入嵌入的28种组合和4种常用的深度学习模型和5种预处理的语言模型。我们的复制研究证实，深度学习总体上可以很好地检测基于社交媒体的自杀性检测，但这在很大程度上取决于数据集的质量。
For preventing youth suicide, social media platforms have received much attention from researchers. A few researches apply machine learning, or deep learning-based text classification approaches to classify social media posts containing suicidality risk. This paper replicated competitive social media-based suicidality detection/prediction models. We evaluated the feasibility of detecting suicidal ideation using multiple datasets and different state-of-the-art deep learning models, RNN-, CNN-, and Attention-based models. Using two suicidality evaluation datasets, we evaluated 28 combinations of 7 input embeddings with 4 commonly used deep learning models and 5 pretrained language models in quantitative and qualitative ways. Our replication study confirms that deep learning works well for social media-based suicidality detection in general, but it highly depends on the dataset's quality.
