• Prof. Krishnendu Nair
• Jadhav Shweta Sanjayrao
• Pathare Komal Bhagwan
• Pachpute Sakshi Sunil
• Rai Anupam

With the exponential growth in the number of social media users over the past few years, social media has become a primary source of information. Especially among young people, the use of social media platforms, such as WhatsApp, Instagram, and Facebook, to share their thoughts and feelings is becoming increasingly popular. Consequently, there has been a growing research interest in the detection of mental illness by analyzing the user- generated textual content of social media posts. Social media activities include messages, watchlists, comments on posts, tag lists, and many more. So artificial intelligence (AI) and machine learning (ML)-based models will detect the mental health of the individual. Then it will predict whether the individual is depressed or not. And then accordingly, it will recommend different mental health sites and resources, like psychiatrists, therapists, and hospitals. For instance, deep learning-based methods such as LSTM are gradually replacing feature engineering-based methods because deep learning frameworks enable models to automatically capture features without the need for time-consuming feature engineering. In addition, pre-trained language models, when trained on mental healthcare datasets, have achieved competitive performance on mental illness detection, which demonstrates their potential value.

Mental Health, Social Media, Deep Learning

"Mental Health Detection based on social media activities", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.f781-f789, February-2024, Available :http://www.jetir.org/papers/JETIR2402594.pdf

"Mental Health Detection based on social media activities", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppf781-f789, February-2024, Available at : http://www.jetir.org/papers/JETIR2402594.pdf