Authors : C. H. Lakshmi; Chiranjeevi Bojja; Dikkolu Veera Pradeep Kumar; Amballa Bala Sai; Sahith Bukkapatnam

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/3sbbm3wk

Scribd : https://tinyurl.com/mrxnu5vm

DOI : https://doi.org/10.38124/ijisrt/26apr1978

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Mental health has become a critical global concern, particularly after the COVID-19 pandemic, as people increasingly share their emotions and psychological states on social media platforms. This paper explores an approach for the early identification of mental health conditions, using textual data gathered from platforms like Twitter, Facebook, and Reddit. The study utilizes various machine learning (ML) techniques like XGBoost, alongside deep learning (DL) models such as BiLSTM, Convolutional Neural Networks (CNN), and RoBERTa. These are applied to user-generated text to pinpoint patterns linked to mental health disorders. These models are crucial for effectively understanding both the contextual meaning and the sequential flow within text data. The system can predict conditions like depression, anxiety, bipolar disorder, and ADHD, which in turn supports earlier awareness and intervention. Ultimately, this project highlights the significant potential of combining ML and DL techniques for scalable and efficient mental health screening, paving the way for improved public health monitoring and decision-making.

Keywords : Mental Health, Machine Learning, Deep Learning, Social Media, Depression, Anxiety, Bipolar, ADHD, Predictive Analytics, BiLSTM, XGBOOST, CNN.

References :

1. WHO         (2022). World       Mental   health Report https://www.who.intlteams/mental-health-and-substance-use/world-mental- health-report.
2. Ferdous, M., Debnath, J. and Chakraborty, N.R., 2020, July. Machine learning algorithms in healthcare: A literature survey. In 2020 11th International conference on computing, communication and networking technologies (ICCCNT) (pp. 1–6). IEEE.
3. Shyam, R. and Singh, R., 2021. A taxonomy of machine learning techniques. J. Adv. Robot, 8 (3), pp. 18–25.
4. Ferdous, M., Debnath, J. and Chakraborty, N.R., 2020, July. Machine learning algorithms in healthcare: A literature survey. In 2020 11th International conference on computing, communication and networking technologies (ICCCNT) (pp. 1–6). IEEE.
5. Sumathy, B., Anand Kumar, D. Sungeetha, Arshad Hashmi, Ankur Saxena, Piyush Kumar Shukla, and Stephen Jeswinde Nuagah. “Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System.” Computational Intelligence and Neuroscience 2022 (2022).
6. Malhotra, Anshu, and Rajni Jindal. “Multimodal deep learning-based framework for detecting depression and suicidal behaviour by affective analysis of social media posts.” EAI Endorsed Transactions on Pervasive Health and Technology 6, no. 21 (2020).
7. Malik, A., Shabaz, M. and Asenso, E., 2023. Machine learning based model for detecting depression during Covid-19 crisis. Scientific African, 20, p. e01716.
8. Banna, M.H.A., Ghosh, T., Nahian, M.J.A., Kaiser, M.S., Mahmud, M., Taher, K.A., Hossain, M.S. and Andersson, K., 2023. A hybrid deep learning model to predict the impact of COVID-19 on mental health from social media big data. IEEE Access, 11, pp. 77009–77022.
9. Tyagi, A., Singh, V.P. and Gore, M.M., 2023. Towards artificial intelligence in mental health: a comprehensive survey on the detection of schizophrenia. Multimedia             Tools      and Applications, 82 (13), pp. 20343–20405.
10. Wainberg, M.L., Scorza, P., Shultz, J.M., Helpmann, L., Mootz, J.J., Johnson, K.A., Neria, Y., Bradford, J.M.E., Oquendo, M.A. and Arbuckle, M.R., 2017. Challenges and opportunities in global mental health: a research-to-practice perspective. Current psychiatry reports, 19, pp. 1–10.