Enhancing Suicide Risk Detection on Social Media through Semi-Supervised Deep Label Smoothing
arxiv(2024)
摘要
Suicide is a prominent issue in society. Unfortunately, many people at risk
for suicide do not receive the support required. Barriers to people receiving
support include social stigma and lack of access to mental health care. With
the popularity of social media, people have turned to online forums, such as
Reddit to express their feelings and seek support. This provides the
opportunity to support people with the aid of artificial intelligence. Social
media posts can be classified, using text classification, to help connect
people with professional help. However, these systems fail to account for the
inherent uncertainty in classifying mental health conditions. Unlike other
areas of healthcare, mental health conditions have no objective measurements of
disease often relying on expert opinion. Thus when formulating deep learning
problems involving mental health, using hard, binary labels does not accurately
represent the true nature of the data. In these settings, where human experts
may disagree, fuzzy or soft labels may be more appropriate. The current work
introduces a novel label smoothing method which we use to capture any
uncertainty within the data. We test our approach on a five-label multi-class
classification problem. We show, our semi-supervised deep label smoothing
method improves classification accuracy above the existing state of the art.
Where existing research reports an accuracy of 43% on the Reddit C-SSRS
dataset, using empirical experiments to evaluate our novel label smoothing
method, we improve upon this existing benchmark to 52%. These improvements in
model performance have the potential to better support those experiencing
mental distress. Future work should explore the use of probabilistic methods in
both natural language processing and quantifying contributions of both
epistemic and aleatoric uncertainty in noisy datasets.
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