Expert, Crowdsourced, and Machine Assessment of Suicide Risk via
            Online Postings

Shing, Han-Chin; Nair, Suraj; Zirikly, Ayah; Friedenberg, Meir; Daumé, Hal; Resnik, Philip

doi:10.18653/v1/w18-0603

Cited by 160 publications

(273 citation statements)

References 42 publications

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“…Analysis of the content on Reddit can be leveraged to help an MHP develop an insight into the current situation of an individual, to improve the quality of the diagnosis and intervention strategies if necessary. Shing et al [54] analyzed the postings of users in SuicideWatch and other related subreddits (e.g., r/bipolarreddit, r/EatingDisorder, r/getting_over_it, and r/socialanxiety) for assessment of suicide risk. The critical opportunity to improve upon these efforts is to utilize reliable domain-specific knowledge sources for understanding the content from a clinical perspective.…”

Section: Figure 1: Changing Suicide Risk Of 3 Redditors Over a Periodmentioning

confidence: 99%

“…Prior research on suicide risk assessment employs four-label (no risk, low risk, moderate risk, and high risk) classification scheme for categorization of suicidal users [54]. In this research, we provide a C-SSRS-based five-label (supportive, indicator, ideation, behavior, and attempt) classification scheme guided by clinical psychiatrists, which allows the MHP to determine an actionable measure of an individual's suicidality and appropriate care [65].…”

Section: Figure 1: Changing Suicide Risk Of 3 Redditors Over a Periodmentioning

confidence: 99%

See 1 more Smart Citation

Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention

Gaur¹,

Alambo²,

Sain³

et al. 2019

The World Wide Web Conference

120

158

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Mental health illness such as depression is a significant risk factor for suicide ideation, behaviors, and attempts. A report by Substance Abuse and Mental Health Services Administration (SAMHSA) shows that 80% of the patients suffering from Borderline Personality Disorder (BPD) have suicidal behavior, 5-10% of whom commit suicide. While multiple initiatives have been developed and implemented for suicide prevention, a key challenge has been the social stigma associated with mental disorders, which deters patients from seeking help or sharing their experiences directly with others including clinicians. This is particularly true for teenagers and younger adults where suicide is the second highest cause of death in the US. Prior research involving surveys and questionnaires (e.g. PHQ-9) for suicide risk prediction failed to provide a quantitative assessment of risk that informed timely clinical decision-making for intervention. Our interdisciplinary study concerns the use of Reddit as an unobtrusive data source for gleaning information about suicidal tendencies and other related mental health conditions afflicting depressed users. We provide details of our learning framework that incorporates domain-specific knowledge to predict the severity of suicide risk for an individual. Our approach involves developing a suicide risk severity lexicon using medical knowledge bases and suicide ontology to detect cues relevant to suicidal thoughts and actions. We also use language modeling, medical entity recognition and normalization and negation detection to create a dataset of 2181 redditors that have discussed or implied suicidal ideation, behavior, or attempt. Given the importance of clinical knowledge, our gold standard dataset of 500 redditors (out of 2181) was developed by four practicing psychiatrists following the guidelines outlined in This paper is published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.

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Section: Figure 1: Changing Suicide Risk Of 3 Redditors Over a Periodmentioning

confidence: 99%

Section: Figure 1: Changing Suicide Risk Of 3 Redditors Over a Periodmentioning

confidence: 99%

Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention

Gaur¹,

Alambo²,

Sain³

et al. 2019

The World Wide Web Conference

120

158

View full text Add to dashboard Cite

show abstract

“…De Choudhury et al [13] applied a logistic regression classifier that led to high accuracy results in order to predict suicidal ideation in Reddit users. One of the first demonstration of suicidice risk assesment through Reddit posts, matched with clinical knowledge was very recently reported by Shing et al [23].…”

Section: Introductionmentioning

confidence: 61%

Understanding Depression from Psycholinguistic Patterns in Social Media Texts

Trifan

Antunes

Matos

2020

Lecture Notes in Computer Science

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The World Health Organization reports that half of all mental illnesses begin by the age of 14. Most of these cases go undetected and untreated. The expanding use of social media has the potential to leverage the early identification of mental health diseases. As data gathered via social media are already digital, they have the ability to power up faster automatic analysis. In this article we evaluate the impact that psycholinguistic patterns can have on a standard machine learning approach for classifying depressed users based on their writings in an online public forum. We combine psycholinguistic features in a rule-based estimator and we evaluate their impact on this classification problem, along with three other standard classifiers. Our results on the Reddit Self-reported Depression Diagnosis dataset outperform some previously reported works on the same dataset. They stand for the importance of extracting psychologically motivated features when processing social media texts with the purpose of studying mental health.

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“…Bi-LSTM is used over sequences of word vectors to capture contextual information between words. Shing et al [1] performed suicide risk assessment of Reddit online posts for specifically subreddit 'SuicideWatch'. They created a dataset of users who posted on subreddit SuicideWatch and their suicide risk level.…”

Section: Related Workmentioning

confidence: 99%

“…An article published by PsychiatryAdvisor in 2016 mentions 6 suicide assessment scales that can be used for suicide risk evaluation of patients 1 . A large number of suicide risk evaluations are conducted through direct contact between an 1 https://bit.ly/2rLLTTg MHP and a patient where the former performs assessments using rating scales or questionnaire [1]. While rating scales such as PHQ-9 2 , or C-SSRS 3 are widely used by the clinical community to assess suicide risk, they can be prone to subjective bias among MHPs.…”

Section: Introductionmentioning

confidence: 99%

Question Answering for Suicide Risk Assessment Using Reddit

Alambo

Lokala

Kursuncu

et al. 2019

2019 IEEE 13th International Conference on Semantic Computing (ICSC)

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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 discussing his/her conditions with an MHP, which leads to inaccurate assessment and treatment of patients. On the other hand, the same patient is comfortable freely discussing his/her mental health condition on social media due to the anonymity of platforms such as Reddit, and the ability to control what, when and how to share.The popular "SuicideWatch" subreddit has been widely used among individuals who experience suicidal thoughts, and provides significant cues for suicidality. The timeliness in sharing thoughts, the flexibility in describing feelings, and the interoperability in using medical terminologies make Reddit an important platform to be utilized as a complementary tool to the conventional healthcare system. As MHPs develop an implicit weighting scheme over the questionnaire (i.e., C-SSRS) to assess suicide risk severity, creating a relative weighting scheme for answers to be automatically generated to the questions in the questionnaire poses as a key challenge.In this interdisciplinary study, we position our approach towards a solution for an automated suicide risk-elicitation framework through a novel question answering mechanism. Our two-fold approach benefits from using: 1) semantic clustering, and 2) sequence-to-sequence (Seq2Seq) models . We also generate a gold standard dataset of suicide posts with their risk levels. This work forms a basis for the next step of building conversational agents that elicit suicide-related natural conversation based on questions.

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Expert, Crowdsourced, and Machine Assessment of Suicide Risk via Online Postings

Cited by 160 publications

References 42 publications

Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention

Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention

Understanding Depression from Psycholinguistic Patterns in Social Media Texts

Question Answering for Suicide Risk Assessment Using Reddit

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