@article{ref1,
title="Hybrid text representation for explainable suicide risk identification on social media",
journal="IEEE transactions on computational social systems",
year="2022",
author="Naseem, Usman and Khushi, Matloob and Kim, Jinman and Dunn, Adam G.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Social media data that characterize users can provide mental health signals, including suicide risks. Existing methods for suicide risk identification on social media have demonstrated promising results; however, the limitation of existing methods is that they are unable to capture low-and high-level features with complex structured data on social media and are incapable of explaining the predicted labels. Explainable models are more useful when translated, so we aimed to evaluate a novel method that would produce explainable models. This article presents a hybrid text representation method that integrates word and document-level text representations to explain suicide risk identification on social media. The proposed method is then fed to a transformer-based encoder with ordinal classification to determine suicide risk. Our results show that our method outperforms state-of-the-art baselines with an FScore of 0.79 (an absolute increase of 15%) on a public suicide dataset. Our method shows that an explainable model can perform at a comparable level to the best nonexplainable models but has advantages if translated for use in clinical and public health practice.<p /> <p>Language: en</p>",
language="en",
issn="2373-7476",
doi="10.1109/TCSS.2022.3184984",
url="http://dx.doi.org/10.1109/TCSS.2022.3184984"
}