AI-Powered System Quantifies Suicide Indicators and Identifies Suicide-Related Content in Online Posts
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Abstract
Suicide is a serious public health concern worldwide, and the rise of social media and online platforms has brought new challenges in identifying and preventing suicidal behaviors. In the past, identifying suicide indicators and related content in online posts relied on human moderators or mental health professionals to manually review and categorize content. This manual approach was both labour-intensive and often lacked real-time capabilities, leading to delays in providing support to individuals in distress. Moreover, the scale of online content made it difficult for traditional methods to handle the ever-increasing volume of information. As a result, this project develops AI-powered system stems from the urgency to tackle the growing issue of suicide in the digital age, which can process data at a scale and speed that exceeds human capabilities, enabling it to analyze many posts, recognize patterns, and detect potential suicide indicators in real-time. This proposed AI-powered system's ability to process and analyze large-scale data in real-time allows for early detection and timely intervention, significantly improving the effectiveness of suicide prevention efforts. The main aim is to find a strong co-relation between components in the subsystem and compare the accuracies to build an alarming system. “Better late than never” the victim can be saved by the proposed method and immediate treatment can be started. Further, this AI-powered system holds great promise in revolutionizing the field of mental health care by enabling more proactive and personalized support for individuals at risk of suicide. Through continuous refinement and development, it is hoped that this technology will play a crucial role in saving lives and promoting mental well-being in an increasingly connected world.
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