A Quantitative and Qualitative Analysis of Suicide Ideation Detection
using Deep Learning
- URL: http://arxiv.org/abs/2206.08673v1
- Date: Fri, 17 Jun 2022 10:23:37 GMT
- Title: A Quantitative and Qualitative Analysis of Suicide Ideation Detection
using Deep Learning
- Authors: Siqu Long, Rina Cabral, Josiah Poon, Soyeon Caren Han
- Abstract summary: 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.
- Score: 5.192118773220605
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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