Enhancing Suicide Risk Detection on Social Media through Semi-Supervised Deep Label Smoothing

• URL: http://arxiv.org/abs/2405.05795v1
• Date: Thu, 9 May 2024 14:25:25 GMT
• Title: Enhancing Suicide Risk Detection on Social Media through Semi-Supervised Deep Label Smoothing
• Authors: Matthew Squires, Xiaohui Tao, Soman Elangovan, U Rajendra Acharya, Raj Gururajan, Haoran Xie, Xujuan Zhou,
• Abstract summary: 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. Social media posts can be classified, using text classification, to help connect people with professional help. These systems fail to account for the inherent uncertainty in classifying mental health conditions.
• Score: 15.1082838217225
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Conceptualizing Suicidal Behavior: Utilizing Explanations of Predicted Outcomes to Analyze Longitudinal Social Media Data [2.76101452577748]
  The COVID-19 pandemic has escalated mental health crises worldwide. Suicide can result from social factors such as shame, abuse, abandonment, and mental health conditions like depression. As these conditions develop, signs of suicidal ideation may manifest in social media interactions.
  arXiv  Detail & Related papers  (2023-12-13T17:15:12Z)
• Semantic Similarity Models for Depression Severity Estimation [53.72188878602294]
  This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings. We use test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels. We evaluate our methods on two Reddit-based benchmarks, achieving 30% improvement over state of the art in terms of measuring depression severity.
  arXiv  Detail & Related papers  (2022-11-14T18:47:26Z)
• Adapting to Online Label Shift with Provable Guarantees [137.89382409682233]
  We formulate and investigate the problem of online label shift. The non-stationarity and lack of supervision make the problem challenging to be tackled. Our algorithms enjoy optimal dynamic regret, indicating that performance is competitive with a clairvoyant nature.
  arXiv  Detail & Related papers  (2022-07-05T15:43:14Z)
• Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning [55.653944436488786]
  According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD. This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
  arXiv  Detail & Related papers  (2022-07-03T11:33:52Z)
• Delving into Probabilistic Uncertainty for Unsupervised Domain Adaptive Person Re-Identification [54.174146346387204]
  We propose an approach named probabilistic uncertainty guided progressive label refinery (P$2$LR) for domain adaptive person re-identification. A quantitative criterion is established to measure the uncertainty of pseudo labels and facilitate the network training. Our method outperforms the baseline by 6.5% mAP on the Duke2Market task, while surpassing the state-of-the-art method by 2.5% mAP on the Market2MSMT task.
  arXiv  Detail & Related papers  (2021-12-28T07:40:12Z)
• An ensemble deep learning technique for detecting suicidal ideation from posts in social media platforms [0.0]
  This paper proposes a LSTM-Attention-CNN combined model to analyze social media submissions to detect suicidal intentions. The proposed model demonstrated an accuracy of 90.3 percent and an F1-score of 92.6 percent.
  arXiv  Detail & Related papers  (2021-12-17T15:34:03Z)
• Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data [74.60507696087966]
  Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care. One promising data source to help monitor human behavior is daily smartphone usage. We study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors.
  arXiv  Detail & Related papers  (2021-06-24T17:46:03Z)
• Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction [0.0]
  Early detection of suicidal ideation in depressed individuals can allow for adequate medical attention and support. Recent NLP research focuses on classifying, from a given piece of text, if an individual is suicidal or clinically healthy. We propose SDCNL, a suicide versus classification method through a deep learning approach.
  arXiv  Detail & Related papers  (2021-02-18T15:40:07Z)
• Detection and Classification of mental illnesses on social media using RoBERTa [0.3753841394482697]
  In this work, we detect and classify five prominent kinds of mental illnesses: depression, anxiety, bipolar disorder, ADHD and PTSD. We believe that our work is the first multi-class model that uses a Transformer-based architecture such as RoBERTa to analyze people's emotions and psychology.
  arXiv  Detail & Related papers  (2020-11-23T05:54:46Z)
• MET: Multimodal Perception of Engagement for Telehealth [52.54282887530756]
  We present MET, a learning-based algorithm for perceiving a human's level of engagement from videos. We release a new dataset, MEDICA, for mental health patient engagement detection.
  arXiv  Detail & Related papers  (2020-11-17T15:18:38Z)
• Towards Preemptive Detection of Depression and Anxiety in Twitter [14.877991297466174]
  Depression and anxiety are psychiatric disorders that are observed in many areas of everyday life. We develop a dataset designed to foster research in depression and anxiety detection in Twitter.
  arXiv  Detail & Related papers  (2020-11-10T17:17:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.