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arXiv · 2206.08673

A Quantitative and Qualitative Analysis of Suicide Ideation Detection using Deep Learning

Siqu Long·Rina Cabral·Josiah Poon·Soyeon Caren Han
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Abstract

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.

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Siqu Long, Rina Cabral, Josiah Poon, Soyeon Caren Han. 2022-06-17. A Quantitative and Qualitative Analysis of Suicide Ideation Detection using Deep Learning. https://arxiv.org/abs/2206.08673

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