Detection of Suicide Ideation in Social Media Forums Using Deep Learning

Tadesse, Michael M.; Lin, Hongfei; Xu, Bo; Yang, Liang

doi:10.3390/a13010007

Cited by 119 publications

(64 citation statements)

References 54 publications

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“…The authors used the CNN-LSTM hybrid model to execute their system. As far [29], a paper has already been completed with this hybrid model using English Dataset. But the new thing in this paper is that the authors used the same hybrid model using the Bangla dataset.…”

Section: Discussionmentioning

confidence: 99%

Depressed People Detection from Bangla Social Media Status using LSTM and CNN Approach

2021

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At present, depression is the main reason for suicidal death. Depression also causes different kinds of diseases. Nowadays, people are deeply involved in social media and like to share their feelings on social media. So, it becomes easy to analyze depression through social media. In this paper, a combination of two CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) models has been proposed to make a hybrid CNN-LSTM model, CNN has performed for the image to create a matrix, and LSTM has given the result from the given matrix. In this paper, datasets are prepared based on depression and non-depression-related status. The proposed method has been applied to that dataset. The best result has been obtained using a hybrid neural network with the word embedding technique using the Bengali Facebook status dataset. We have used the SVM (Support Vector Machine) model to predict a small dataset of Bengali Facebook status and count vectorizer to count the word in the document. Finally, this paper has built up a model that makes strength and support for deep learning architecture.

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Section: Discussionmentioning

confidence: 99%

Depressed People Detection from Bangla Social Media Status using LSTM and CNN Approach

2021

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“…This system still needs improvement by adding data about phrases that lead to suicidal ideas and improving the possibility of bias in translating phrases that appear on the internet. 14 Community Based Program. According to Ono et.al.…”

Section: Discussionmentioning

confidence: 99%

Mass Therapeutic Education for Monitoring Suicidal Behavior in Community

Sofyan¹

2021

JPPBR

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Background: Suicide is a worrying problem in Indonesia because of increase in case reported. There has been an increase in suicide rates both globally and in Indonesia. The negative stigma, lack of education, and lack of understanding of the role of the community make it difficult to reduce the number of suicides. This study aims to discuss mass therapeutic education for monitoring suicidal behavior in community. Method: Researchers used 15 journals and literature that discuss the impact and vulnerability of distance learning on students' mental health conditions. Conclusion: The role of society in reducing the suicide rate is very much needed. The community can help make early detection of suicide. Society can help prevent suicidal ideas from arising in those around them. The public can provide information to the authorities and give advice for suicide perpetrators to visit a health center. The role of the community can be integrated in a structured and neat system that can make suicide prevention efforts become optimal.

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“…One model, using a combination of long short-term memory and convolutional neural networks, achieved an accuracy and precision of 93% in identifying users with suicidal ideation. 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.…”

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

confidence: 98%

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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Detection of Suicide Ideation in Social Media Forums Using Deep Learning

Cited by 119 publications

References 54 publications

Depressed People Detection from Bangla Social Media Status using LSTM and CNN Approach

Depressed People Detection from Bangla Social Media Status using LSTM and CNN Approach

Mass Therapeutic Education for Monitoring Suicidal Behavior in Community

AI enabled suicide prediction tools: a qualitative narrative review

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