Supervised Learning for Suicidal Ideation Detection in Online User Content

Ji, Shaoxiong; Yu, Celina P.; Fung, Sai-fu; Pan, Shirui; Long, Guodong

doi:10.1155/2018/6157249

Cited by 137 publications

(134 citation statements)

References 35 publications

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“…Our results show that the proposed combined neural network model performs better in comparison to single LSTM and CNN classifiers. Concerning the accuracy performance, our results outperform the accuracy of the experiment previously applied on the same dataset [31].…”

Section: Classification Analysis Resultsmentioning

confidence: 46%

“…Sawhney et al [30] work revealed the strength and ability of C-LSTM-based models as compared to other deep learning and machine learning classifiers for suicide ideation recognition. Ji et al [31] compared the LSTM classifier with five other machine learning models and demonstrated the feasibility and practicability of the approaches. His study provides one of the major benchmarks for the detection of suicide ideation on Reddit SuicideWatch and Twitter.…”

Section: Background and Related Workmentioning

confidence: 99%

“…They engage with each other via comment threads attached to each post [9]. The dataset used in our experiment was built by Ji et al [31] and consists of a list of suicide-indicative and non-suicidal posts. To preserve the users' privacy, their personal information is replaced with a unique ID.…”

Section: Datasetsmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Classification Analysis Resultsmentioning

confidence: 46%

Section: Background and Related Workmentioning

confidence: 99%

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Detection of Suicide Ideation in Social Media Forums Using Deep Learning

et al. 2019

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“…This baseline is a rule-based model for classifying a user based on a strict and soft match criteria according to presence of a concept in the user's content and the suicide risk severity lexicon. For a competitive baseline, we compared this baseline with word-embedding and TF-IDF based approaches for suicide classification [29]. As we also experimented with word-embedding models trained over suicide and non-suicide related content, using compositions of word vectors [3,32,41], the baseline based on suicide risk severity lexicon outperformed these competitive approaches.…”

Section: Baselinesmentioning

confidence: 99%

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

Gaur¹,

Alambo²,

Sain³

et al. 2019

The World Wide Web Conference

116

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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“…30 Ji et al found comparable results, demonstrating that machine learning techniques could leverage statistical, linguistic, word embedding and topic features to achieve 90% accuracy in identifying suicide ideation on Reddit and Twitter. 31 Despite these promising results, the utility of identifying suicidal ideation may be limited due to low positive predictive value and modest sensitivity for suicide attempts. This is due to the low incidence of suicide attempts when compared with incidence of suicidal ideation.…”

Section: Ai-driven Prediction Relating To Suicide Risk Factors Suicidmentioning

confidence: 99%

AI enabled suicide prediction tools: a qualitative narrative review

D’Hotman

Loh

2020

BMJ Health Care Inform

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Background: Suicide poses a significant health burden worldwide. In many cases, people at risk of suicide do not engage with their doctor or community due to concerns about stigmatisation and forced medical treatment; worse still, people with mental illness (who form a majority of people who die from suicide) may have poor insight into their mental state, and not self-identify as being at risk. These issues are exacerbated by the fact that doctors have difficulty in identifying those at risk of suicide when they do present to medical services. Advances in artificial intelligence (AI) present opportunities for the development of novel tools for predicting suicide.Method: We searched Google Scholar and PubMed for articles relating to suicide prediction using artificial intelligence from 2017 onwards.Conclusions: This paper presents a qualitative narrative review of research focusing on two categories of suicide prediction tools: medical suicide prediction and social suicide prediction. Initial evidence is promising: AI-driven suicide prediction could improve our capacity to identify those at risk of suicide, and, potentially, save lives. Medical suicide prediction may be relatively uncontroversial when it pays respect to ethical and legal principles; however, further research is required to determine the validity of these tools in different contexts. Social suicide prediction offers an exciting opportunity to help identify suicide risk among those who do not engage with traditional health services. Yet, efforts by private companies such as Facebook to use online data for suicide prediction should be the subject of independent review and oversight to confirm safety, effectiveness and ethical permissibility.

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Supervised Learning for Suicidal Ideation Detection in Online User Content

Cited by 137 publications

References 35 publications

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

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

AI enabled suicide prediction tools: a qualitative narrative review

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