AI POWERED SYSTEM QUANTIFIES SUICIDE INDICATORS AND IDENTIFIES SUICIDE RELATED CONTENT IN ONLINE POSTS Article Swipe

In the digital age, the pervasive use of social media platforms has opened new avenues forunderstanding human behaviours, particularly mental health. With millions of users posting dailyabout their emotional states, life struggles, and personal experiences, these platforms have becomerich sources of psychological signals—some of which may indicate distress or suicidal intent.Suicide remains a critical public health issue, accounting for nearly 1.3% of all global deathsannually. Early identification and intervention are essential, yet traditional methods rely heavily onself-reporting or clinical interaction, which may not always be timely or accessible. The increasing prevalence of mental health disorders, particularly depression and suicidal ideation,has become a critical public health concern. In the digital era, social media platforms such asTwitter, Reddit, and Facebook have become modern diaries where individuals often express theirthoughts, emotions, and personal struggles. These platforms present an Opportunity for leveragingArtificial Intelligence (AI) to identify warning signs of suicide and initiate early intervention. Thisproject proposes a robust AI-powered system that quantitatively analyses user-generated onlinecontent to detect suicide-related indicators with high accuracy. By integrating Natural LanguageProcessing (NLP), sentiment analysis, and deep learning techniques, the system processes andclassifies large volumes of unstructured text to distinguish between suicidal and non-suicidal posts.The system utilizes advanced contextual models like BERT to understand the emotional tone andsemantic meaning of the content, allowing it to detect both explicit and implicit cues of suicidalideation. A custom suicide risk scoring algorithm evaluates each post based on keyword patterns,emotional intensity, polarity shifts, and linguistic behaviors indicative of psychological distress. Theflagged content is then prioritized for alert generation, which can be reviewed by mental healthprofessionals or support organizations for timely intervention. This approach demonstrates the potential of AI in contributing to suicide prevention efforts byproviding scalable, non-invasive, and real-time monitoring of digital platforms. The system wasevaluated on publicly available mental health datasets and achieved promising results in terms ofprecision, recall, and overall detection performance. The outcomes of this project highlight theeffectiveness of AI-driven mental health surveillance systems and pave the way for futureenhancements in automated emotional support tools and digital health initiatives.