Extracting psychiatric stressors for suicide from social media using deep learning

Du, Jingcheng; Zhang, Yaoyun; Luo, Jianhong; Jia, Yuxi; Wei, Qiang; Tao, Cui; Xu, Hua

doi:10.1186/s12911-018-0632-8

Cited by 128 publications

(71 citation statements)

References 28 publications

(25 reference statements)

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“…Amini et al utilized SVM, and decision trees besides RF and Neural Networks (NN), for assessing the risk of suicide in a dataset of individuals from Iran [2]. A recent study by Du et al [19] used deep learning methods to detect psychiatric stressors leading to suicide. They built binary classifier for identifying suicidal tweets from non-suicidal tweets using Convolutional Neural Networks (CNN).…”

Section: Models For Suicide Predictionmentioning

confidence: 99%

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

Gaur¹,

Alambo²,

Sain³

et al. 2019

The World Wide Web Conference

103

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: Models For Suicide Predictionmentioning

confidence: 99%

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

Gaur¹,

Alambo²,

Sain³

et al. 2019

The World Wide Web Conference

103

158

View full text Add to dashboard Cite

show abstract

“…Bhat et al [36] and [37] highlighted CNN's performance over other approaches to identify the presence of suicidal tendencies among adolescents. Du et al [38] applied deep learning methods to detect psychiatric stressors for suicide recognition in social media. Using CNN networks, he built a binary classifier to separate suicidal tweets from non-suicidal tweets.…”

Section: Background and Related Workmentioning

confidence: 99%

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

et al. 2019

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Suicide ideation expressed in social media has an impact on language usage. Many at-risk individuals use social forum platforms to discuss their problems or get access to information on similar tasks. The key objective of our study is to present ongoing work on automatic recognition of suicidal posts. We address the early detection of suicide ideation through deep learning and machine learning-based classification approaches applied to Reddit social media. For such purpose, we employ an LSTM-CNN combined model to evaluate and compare to other classification models. Our experiment shows the combined neural network architecture with word embedding techniques can achieve the best relevance classification results. Additionally, our results support the strength and ability of deep learning architectures to build an effective model for a suicide risk assessment in various text classification tasks.

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“…They employed the martingale framework to capture the behavioral changes present in Twitter posts through a combination of textual and behavioral features. Du et al [17] used deep learning methods to detect psychiatric stressors leading to suicide. They built binary classifier for identifying suicidal tweets from non-suicidal tweets using Convolutional Neural Networks (CNNs).…”

Section: Related Workmentioning

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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Extracting psychiatric stressors for suicide from social media using deep learning

Cited by 128 publications

References 28 publications

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

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

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

Question Answering for Suicide Risk Assessment Using Reddit

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