Detection of Suicide Ideation in Social Media Forums Using Deep Learning

Michael Mesfin Tadesse · Hongfei Lin · Bo Xu · Liang Yang

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Abstract

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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2021Predicting Depression via Social Media 1975Hopelessness and suicidal behavior. An overview 2013Differentiating Suicide Attempters from Suicide Ideators: A Critical Frontier for Suicidology Research 2016Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media 2001Word Use in the Poetry of Suicidal and Nonsuicidal Poets 2015Detecting suicidality on Twitter 2018Deep Learning for Depression Detection of Twitter Users 2013Tracking Suicide Risk Factors Through Twitter in the US

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2022Detecting and Analyzing Suicidal Ideation on Social Media Using Deep Learning and Machine Learning Models 2020Using Social Media for Mental Health Surveillance 2023Mental Health Analysis in Social Media Posts: A Survey 2023Suicide Risk Assessments Through the Eyes of ChatGPT-3.5 Versus ChatGPT-4: Vignette Study 2022A Novel Text Mining Approach for Mental Health Prediction Using Bi-LSTM and BERT Model 2022

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