NLP-ML Hybrid: Identifying Signs of Suicidal Thoughts in Social Media Content

The increasing prevalence of suicide globally necessitates innovative approaches to early detection and prevention. Social media, where individuals often express personal distress, presents a unique opportunity for intervention. Current techniques for identifying suicidal thoughts on social media platforms face challenges with precision, the ability to scale effectively, and the capability to adapt to a wide range of linguistic styles. Traditional approaches primarily rely on keyword-based searches and simple statistical techniques, which often fail to capture the subtle context and language indicative of suicidal ideation. This study introduces a novel hybrid method that combines BERT, BiLSTMs, and CNNs. This combination aims to improve detection accuracy by delving into more complex linguistic patterns and contextual understanding. We utilized a comprehensive dataset from Reddit, consisting of over $\mathbf{5 0, 0 0 0}$ posts labeled for signs of suicidal thoughts, curated to train and validate our models. The proposed models significantly outperform traditional methods, with BERT achieving an unprecedented accuracy of $\mathbf{9 6. 9 9 \%}$, demonstrating robustness in identifying nuanced expressions of suicide ideation. This work presents an effective and scalable method for identifying suicidal thoughts at an early stage, offering a significant advancement in the field of digital mental health. By identifying individuals at risk sooner, this technology can facilitate timely support and interventions, potentially saving lives.