Detecting Suicide Risk Across Social Media Platforms: Comparing Traditional and Deep Learning Approaches

Suicide is an undeniable threat to our society today. People of all ages are prone to committing suicide. Most of them remain in depression before committing suicide. They often share their posts and thoughts on social networks to express the suffering they are going through. This research focuses on identifying suicide risk through social media content using AI-based approaches. We have collected Twitter and Reddit posts labeled as suicidal or non-suicidal, and Natural Language Processing (NLP) techniques are applied for preprocessing and feature extraction. Various models, including traditional machine learning (SVM, Random Forest, etc.) and deep learning approaches (CNN, ANN, LSTM, BERT) and hybrid(BERTCNN, CNN-LSTM), were trained and evaluated. Our results show that deep learning models, especially BERT and CNNLSTM, outperform traditional classifiers 93.51% in SVM and Logistic Regression), achieving up to 98.12% accuracy. This study demonstrates the potential of AI in real-time mental health monitoring and early suicide prevention through social media analysis. Timely detection of suicidal intent can mean the difference between life and death.