Suicidal risk identification in social media

J, Ashok Kumar; Trueman, Tina Esther; Abinesh, A K

doi:10.1016/j.procs.2021.05.106

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…The second most common cause of death for those between the ages of 10 and 34 is suicide. The prevalence of suicide is rising quickly due to mental health issues such as anxiety and depression [1]. Social media today has drawn billions of individuals to engage in interpersonal communication.…”

Section: Introductionmentioning

confidence: 99%

Suicidal Ideation Detection from social media: A Detailed Review of Machine Learning and Deep Learning

Seby,

Elamparithi,

Anuratha

2023

ijmst

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Social networks are crucial tools for learning about people's attitudes towards various issues since they allow them to express their thoughts to friends and family. Recent years have seen considerable challenges in natural language processing (NLP) and psychology regarding the detection of suicidal ideation via online social network analysis. The complex early signs of suicide ideations can be identified with the proper use of social media information, which can afterwards save many lives. Even though numerous strategies have been used over the past few decades to identify suicidal thoughts, machine learning (ML) and deep learning (DL) methods offer more insightful results. So, in this study, we examine several cutting-edge ML and DL approaches for detecting suicidal ideation. We also observe a few significant issues in the entire corpus of literature that could be explored in further research. Finally, we hope this study will shed light on critical issues in identifying suicidal thoughts on social media for readers and ML and DL researchers.

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Section: Introductionmentioning

confidence: 99%

Suicidal Ideation Detection from social media: A Detailed Review of Machine Learning and Deep Learning

Seby,

Elamparithi,

Anuratha

2023

ijmst

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Analyzing Social Media texts for Suicidal Risk Identification using Natural Language Processing

Rai,

Bhattarai,

Dhar

et al. 2023

2023 9th International Conference on Signal Processing and Communication (ICSC)

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Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk

Parsapoor¹,

Koudys²,

Ruocco³

2023

Front. Psychiatry

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Suicide is a leading cause of death that demands cross-disciplinary research efforts to develop and deploy suicide risk screening tools. Such tools, partly informed by influential suicide theories, can help identify individuals at the greatest risk of suicide and should be able to predict the transition from suicidal thoughts to suicide attempts. Advances in artificial intelligence have revolutionized the development of suicide screening tools and suicide risk detection systems. Thus, various types of AI systems, including text-based systems, have been proposed to identify individuals at risk of suicide. Although these systems have shown acceptable performance, most of them have not incorporated suicide theories in their design. Furthermore, directly applying suicide theories may be difficult because of the diversity and complexity of these theories. To address these challenges, we propose an approach to develop speech- and language-based suicide risk detection systems. We highlight the promise of establishing a benchmark textual and vocal dataset using a standardized speech and language assessment procedure, and research designs that distinguish between the risk factors for suicide attempt above and beyond those for suicidal ideation alone. The benchmark dataset could be used to develop trustworthy machine learning or deep learning-based suicide risk detection systems, ultimately constructing a foundation for vocal and textual-based suicide risk detection systems.

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Suicidal risk identification in social media

Cited by 4 publications

References 13 publications

Suicidal Ideation Detection from social media: A Detailed Review of Machine Learning and Deep Learning

Suicidal Ideation Detection from social media: A Detailed Review of Machine Learning and Deep Learning

Analyzing Social Media texts for Suicidal Risk Identification using Natural Language Processing

Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk

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