A Quantitative and Qualitative Analysis of Suicide Ideation Detection using Deep Learning
This incremental study addresses youth suicide prevention by analyzing detection methods, but it replicates existing approaches without introducing new techniques.
The paper tackled the problem of detecting suicidal ideation from social media posts by replicating and evaluating deep learning models, finding that deep learning generally works well but performance heavily depends on dataset quality.
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.