This paper discusses the use of machine learning techniques for predicting and identifying suicide risk on social networking websites and suggests an approach that involves analyzing social media posts using different machine learning algorithms, including deep learning models, to detect suicidal ideation. The effectiveness of the model is evaluated using various metrics such as precision, recall, accuracy, and F1-score. The results show that machine learning techniques can successfully identify individuals at risk of suicide. The findings are significant for mental health professionals, social media companies, and individuals at risk of suicide, and contribute to the ongoing efforts to use technology for suicide prevention and improved mental health outcomes.

Machine Learning, Predictive Modeling, Social Media, Suicide Prevention, Text

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