A MENTAL HEALTH SURVEILLANCE SYSTEM USING AI ON SOCIAL MEDIA POSTS

Mr. A. SRINIVASA REDDY, KORRA MAHESH, NAMANI KAMAL, K. NITHIN REDDY, NALLA MAYUR

doi:10.62643/

Authors

Mr. A. SRINIVASA REDDY, KORRA MAHESH, NAMANI KAMAL, K. NITHIN REDDY, NALLA MAYUR Author

DOI:

https://doi.org/10.62643/

Keywords:

Suicide detection, Depression classification, social media analysis, Suicidal ideation, Artificial intelligence, Machine learning, Natural language processing, Text mining, Emotional analysis, Risk prediction

Abstract

Suicide, particularly among young individuals, has become a critical global concern, with increasing rates posing a challenge to mental health professionals and society at large. Understanding the underlying factors and early warning signs of suicidal ideation is vital for effective prevention. In the digital age, social media platforms serve as an outlet for users to express their thoughts, emotions, and daily activities, offering valuable insights into their mental well-being. This study explores the use of Artificial Intelligence (AI), Machine Learning (ML) and Natural Language Toolkit (NLTK) techniques to analyze social media content for detecting signs of depression and potential suicidal tendencies. By examining patterns in users posts, the research aims to identify indicators of emotional distress and predict suicidal risk with greater accuracy. The findings reveal a strong correlation between depressive expressions on social media and suicidal ideation, highlighting the feasibility of early detection through online behavior analysis. This research contributes to the development of proactive suicide prevention strategies, emphasizing the potential of AI-driven social media monitoring tools to support at-risk individuals and reduce suicide rates.