Suicidal Behaviour Detection and Prevention

Abstract: The global use of social media has reshaped human relationship with liberty to exchange experience and emotions without limitation. Free communication with the risk of suicidal content, prevalent among the youth generation, requires efficient monitoring and intervention. The project currently utilizes the BERT-based deep learning models to detect and prevent suicide risk based on text analysis. The system assigns the content either as "Sad" or "Not Sad" depending on whether social media postings are being monitored. If the post is categorized to "Sad," the system sends motivational quotes through SMS and Telegram through Twilio and Telethon following an auto-response setting. The system also forwards all distress posts to a suicide help line for immediate expert intervention. Successful Natural Language Processing (NLP) and artificial communication integration ensures successful suicide risk assessment for successful suicide prevention against catastrophic consequences. Suicidal prevention theory of prevention allows for reinforcement of online tracking of mental state and hence is a vital emotional well-being and crisis therapy