Question Answering for Suicide Risk Assessment Using Reddit

@article{Alambo2019QuestionAF,
  title={Question Answering for Suicide Risk Assessment Using Reddit},
  author={Amanuel Alambo and Manas Gaur and Usha Lokala and Ugur Kursuncu and Krishnaprasad Thirunarayan and Amelie Gyrard and A. Sheth and Randon S. Welton and Jyotishman Pathak},
  journal={2019 IEEE 13th International Conference on Semantic Computing (ICSC)},
  year={2019},
  pages={468-473}
}
Mental Health America designed ten questionnaires that are used to determine the risk of mental disorders. They are also commonly used by Mental Health Professionals (MHPs) to assess suicidality. Specifically, the Columbia Suicide Severity Rating Scale (C-SSRS), a widely used suicide assessment questionnaire, helps MHPs determine the severity of suicide risk and offer an appropriate treatment. A major challenge in suicide treatment is the social stigma wherein the patient feels reluctance in… 

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References

SHOWING 1-10 OF 20 REFERENCES

Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media

This paper develops a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation, and utilizes semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts.

Natural Language Processing of Social Media as Screening for Suicide Risk

The feasibility of using social media data to detect those at risk for suicide, using natural language processing and machine learning techniques to detect quantifiable signals around suicide attempts, and designs for an automated system for estimating suicide risk are described.

The Columbia-Suicide Severity Rating Scale (C-SSRS): Has the "Gold Standard" Become a Liability?

The evidence suggests that the Columbia-Suicide Severity Rating Scale is conceptually and psychometrically flawed and does not map to the Food and Drug Administration's new standards.

The language of mental health problems in social media

The language of Reddit posts specific to mental health is investigated, to define linguistic characteristics that could be helpful for further applications and to demonstrate that there are also condition-specific vocabularies used in social media to communicate about particular disorders.

Instruments for the assessment of suicide risk: A systematic review evaluating the certainty of the evidence

Most suicide risk assessment instruments were supported by too few studies to allow for evaluation of accuracy, and among those that could be evaluated, none fulfilled requirements for sufficient diagnostic accuracy.

Extracting psychiatric stressors for suicide from social media using deep learning

This is the first effort to extract psychiatric stressors from Twitter data using deep learning based approaches and transfer learning strategy which leverages an existing annotation dataset from clinical text to reduce the annotation cost and improve the performance.

Social media as a measurement tool of depression in populations

A social media depression index is introduced that may serve to characterize levels of depression in populations and confirm psychiatric findings and correlate highly with depression statistics reported by the Centers for Disease Control and Prevention (CDC).

Characterisation of mental health conditions in social media using Informed Deep Learning

This study analysed posts from the social media platform Reddit and developed classifiers to recognise and classify posts related to mental illness according to 11 disorder themes, which could automatically recognise mental illness-related posts in the balenced dataset with an accuracy of 91.08% and select the correct theme with a weighted average accuracy of 71.37%.

College Students' Responses to Suicidal Content on Social Networking Sites: An Examination Using a Simulated Facebook Newsfeed.

Overall, results indicate that college students are responsive to suicidal content on Facebook.

"Let Me Tell You About Your Mental Health!": Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention

A novel approach to map each subreddit to the best matching DSM-5 (Diagnostic and Statistical Manual of Mental Disorders - 5th Edition) category using multi-class classifier and a detailed analysis of the nature of subreddit content from domain expert's perspective is provided.