Authors : Suganthi D.; Dr. A. Geetha

Volume/Issue : Volume 11 - 2026, Issue 8 - August

Google Scholar : https://tinyurl.com/49n9uucv

DOI : https://doi.org/10.38124/ijisrt/26aug1322

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

Abstract : Depression is a prevalent mental health disorder that significantly impacts individuals' well-being and daily life. With the increasing use of social media, users often express emotions, thoughts, and behaviours that can indicate mental health conditions, including depression. This study proposes a deep learning-based approach to detect depressive tendencies from social media posts by leveraging advanced Natural Language Processing (NLP) techniques and multimodal data analysis. We utilize transformer-based models such as BERT, RoBERTs, and XLNet for feature extraction and sentiment analysis. Additionally, we explore hybrid deep learning architectures integrating CNNs, LSTMs, and attention mechanisms to enhance contextual understanding. To improve accuracy and generalization, the dataset is augmented using selfsupervised learning techniques and pre-trained embeddings fine-tuned on mental health-related corpora. The model is evaluated on benchmark datasets using precision, recall, F1-score, and AUC-ROC metrics. The results demonstrate that deep learning-based approaches outperform traditional machine learning methods in detecting depressive expressions from social media posts. Furthermore, we discuss the ethical considerations and potential applications of AI-driven depression detection in real-world mental health support systems. Our findings suggest that AI can play a crucial role in early depression detection and intervention, contributing to proactive mental health care solutions.

Keywords : Depression Detection, Social Media, Deep Learning, NLP, Mental Health AI.

References :

1. World Health Organization (WHO). (2023). Postnatal care guidelines: Maternal and newborn health. Retrieved from https://www.who.int
2. CLPsych Shared Task https://clpsych.org
3. American College of Obstetricians and Gynecologists (ACOG). (2022). Postpartum care: Recommendations and best practices. Journal of Obstetric and Gynecological Research, 48(3), 256-270.
4. Smith, J., & Brown, K. (2021). Predictive analytics in maternal health: Machine learning approaches to postnatal care. International Journal of Medical Informatics, 145, 104328.
5. Centers for Disease Control and Prevention (CDC). (2022). Postpartum care and outcomes in the U.S. Retrieved from https://www.cdc.gov
6. Patel, R., Gupta, S., & Sharma, A. (2020). Analyzing postpartum recovery trends using statistical modeling. Health Informatics Journal, 26(4), 789-803.
7. National Institute for Health and Care Excellence (NICE). (2023). Postnatal care recommendations and clinical guidelines. Retrieved from https://www.nice.org.uk
8. Kumar, A., & Mehta, P. (2019). Big data analytics in maternal healthcare: Trends and future directions. Journal of Biomedical Research, 35(2), 112-124.
9. United Nations Population Fund (UNFPA). (2022). Global trends in maternal and newborn health. Retrieved from https://www.unfpa.org
10. Davis, L., & Thompson, M. (2021). Improving postnatal care through data-driven decision-making. Journal of Public Health Informatics, 13(1), 45-58.
11. Miller, H., & Roberts, J. (2020). AI and machine learning applications in postnatal healthcare. Computational Medicine, 28(5), 311-329.