Exploring Deep Learning and Machine Learning Models for Suicidal Ideation Detection on Social Media Platforms

Social media websites have emerged as a source in this digital era wherein understanding and addressing mental health issues is done. This paper takes an analysis tour through what contemporary deep learning and machine learning models are doing in finding suicidal ideation over social media. Further, different currently used techniques will be reviewed in detail, which range from NLP and sentiment analysis to state-of-theart deep learning architectures such as CNNs and LSTMs. The research work involves the collection and preprocessing of data obtained through social media and then the application and comparison of several machine learning algorithms for finding and classifying suicidal ideation indicators. The results of our experiments show that deep learning models, especially those utilizing contextual embeddings from Transformer-based architectures, are much better in terms of both accuracy and robustness compared to state-of-the-art traditional machine learning approaches. Further discussion will be made on various challenges associated with data imbalance, privacy concerns, and ethical consideration issues in development and deployment. It therefore complements ongoing efforts toward the improvement of mechanisms for early detection of suicidal ideation and a deeper insight into how AI-driven solutions could support mental health interventions. The results underline, on one hand, that deep learning combined with machine learning methods can serve to address challenging tasks in social media data, but on the other hand, they also raise awareness of the importance of responsible AI practices in sensitive applications. Future efforts must be directed at the finetuning of these models and seeing how they can be implemented in real mental health support systems.