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.
@article{arxiv.2206.08673,
title = {A Quantitative and Qualitative Analysis of Suicide Ideation Detection using Deep Learning},
author = {Siqu Long and Rina Cabral and Josiah Poon and Soyeon Caren Han},
journal= {arXiv preprint arXiv:2206.08673},
year = {2022}
}