An AI-Driven Hybrid Approach for Detecting Mental Health Indicators in Multilingual Indian Social Media: Data Acquisition and Analytical Frameworks
Received: 29 September 2025 | Revised: 19 October 2025, 31 October 2025, 8 December 2025, and 13 December 2025 | Accepted: 15 December 2025 | Online: 9 February 2026
Corresponding author: Melwin D. Souza
Abstract
Social networks have become the leading platform for human expression and have therefore been helpful in the early detection of psychological distress. This paper presents a multilingual framework to harvest and examine social media posts in a variety of regional Indian languages, such as Tamil, Telugu, Kannada, Malayalam, and English, to identify signs of different mental health disorders, such as depression, anxiety, and stress. The proposed method combines cutting-edge natural language processing and deep learning approaches, with special attention to exploiting Transformer models in conjunction with psychological lexicon-features, to disentangle complex linguistic and emotional patterns in multilingual text. A high-quality dataset, annotated by mental health experts to capture a variety of mental health signs, served as the basis for model training. The preprocessing pipeline addresses the challenges of multilingual, code-mixed, and transliterated text to provide uniform data quality across languages. The evaluation results show that the proposed hybrid model performs better than classical sentiment analysis approaches, achieving higher accuracy in identifying a variety of mental health signs. By identifying subtle emotional and linguistic signals in social media posts, this work illustrates the viability of scalable, real-world mental health monitoring across India's linguistic diversity. Future work includes the extension of language coverage, model interpretability through explainable AI, and real-time application to assist mental health clinicians in early detection and intervention.
Keywords:
multilingual NLP, code-mixed text, lexicon features, mental health signal detection, expert-annotatedDownloads
References
M. Garg, ''Mental Health Analysis in Social Media Posts: A Survey,'' Archives of Computational Methods in Engineering, vol. 30, no. 3, pp. 1819–1842, Apr. 2023. DOI: https://doi.org/10.1007/s11831-022-09863-z
F. Rehmani, Q. Shaheen, M. Anwar, M. Faheem, and S. S. Bhatti, ''Depression detection with machine learning of structural and non‐structural dual languages,'' Healthcare Technology Letters, vol. 11, no. 4, pp. 218–226, Aug. 2024. DOI: https://doi.org/10.1049/htl2.12088
P. Ta, N. Tran, H. Nguyen, and H. D. Nguyen, ''Detecting signs of depression on social media: A machine learning analysis and evaluation,'' Sustainable Futures, vol. 10, Dec. 2025, Art. no. 100827. DOI: https://doi.org/10.1016/j.sftr.2025.100827
V. Vajrobol, N. Aggarwal, U. Shukla, G. J. Saxena, S. Singh, and A. Pundir, ''Explainable cross-lingual depression identification based on multi-head attention networks in Thai context,'' International Journal of Information Technology, vol. 17, no. 5, pp. 2997–3012, June 2025. DOI: https://doi.org/10.1007/s41870-023-01512-3
V. Tejaswini, K. S. Babu, and B. Sahoo, ''Depression Detection from Social Media Text Analysis using Natural Language Processing Techniques and Hybrid Deep Learning Model,'' ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 23, no. 1, pp. 1–20, Jan. 2024. DOI: https://doi.org/10.1145/3569580
Vandana, N. Marriwala, and D. Chaudhary, ''A hybrid model for depression detection using deep learning,'' Measurement: Sensors, vol. 25, Feb. 2023, Art. no. 100587. DOI: https://doi.org/10.1016/j.measen.2022.100587
K. Daly and O. Olukoya, ''Depression detection in read and spontaneous speech: A Multimodal approach for lesser-resourced languages,'' Biomedical Signal Processing and Control, vol. 108, Oct. 2025, Art. no. 107959. DOI: https://doi.org/10.1016/j.bspc.2025.107959
A. Khan and R. Ali, ''Unraveling minds in the digital era: a review on mapping mental health disorders through machine learning techniques using online social media,'' Social Network Analysis and Mining, vol. 14, no. 1, Apr. 2024, Art. no. 78. DOI: https://doi.org/10.1007/s13278-024-01205-0
S. T. Ibrahim, M. Li, J. Patel, and T. R. Katapally, ''Utilizing natural language processing for precision prevention of mental health disorders among youth: A systematic review,'' Computers in Biology and Medicine, vol. 188, Apr. 2025, Art. no. 109859. DOI: https://doi.org/10.1016/j.compbiomed.2025.109859
Y. Cao et al., ''Machine Learning Approaches for Depression Detection on Social Media: A Systematic Review of Biases and Methodological Challenges.,'' Journal of Behavioral Data Science, vol. 5, no. 1, pp. 67–102, Feb. 2025. DOI: https://doi.org/10.35566/jbds/caoyc
T. Amorese et al., ''Detecting depression in speech using verbal behavior analysis: a cross-cultural study,'' Frontiers in Psychology, vol. 16, May 2025, Art. no. 1514918. DOI: https://doi.org/10.3389/fpsyg.2025.1514918
M. K. Myee, R. D. C. Rebekah, T. Deepa, G. D. Zion, and K. Lokesh, ''Detection of Depression in Social Media Posts using Emotional Intensity Analysis,'' Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 16207–16211, Oct. 2024. DOI: https://doi.org/10.48084/etasr.7461
B. G. Teferra et al., ''Screening for Depression Using Natural Language Processing: Literature Review,'' Interactive Journal of Medical Research, vol. 13, Nov. 2024, Art. no. e55067. DOI: https://doi.org/10.2196/55067
M. E. Aragón, A. P. López-Monroy, M. Montes-y-Gómez, and D. E. Losada, ''Adapting language models for mental health analysis on social media,'' Artificial Intelligence in Medicine, vol. 168, Oct. 2025, Art. no. 103217. DOI: https://doi.org/10.1016/j.artmed.2025.103217
X. Shi, X. Liu, C. Xu, Y. Huang, F. Chen, and S. Zhu, ''Cross-lingual offensive speech identification with transfer learning for low-resource languages,'' Computers and Electrical Engineering, vol. 101, July 2022, Art. no. 108005. DOI: https://doi.org/10.1016/j.compeleceng.2022.108005
M. Kanahuati-Ceballos and L. J. Valdivia, ''Detection of depressive comments on social media using RNN, LSTM, and random forest: comparison and optimization,'' Social Network Analysis and Mining, vol. 14, no. 1, Feb. 2024, Art. no. 44. DOI: https://doi.org/10.1007/s13278-024-01206-z
W. B. Tahir, S. Khalid, S. Almutairi, M. Abohashrh, S. A. Memon, and J. Khan, ''Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning Techniques,'' IEEE Access, vol. 13, pp. 12789–12818, 2025. DOI: https://doi.org/10.1109/ACCESS.2025.3530862
T. S. Kumar, ''A Deep Learning Framework with a Hybrid Model for Automatic Depression Detection in Social Media Posts,'' International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 4, pp. 3217–3231, June 2024.
A. Gupta and R. Katarya, ''Social media based surveillance systems for healthcare using machine learning: A systematic review,'' Journal of Biomedical Informatics, vol. 108, Aug. 2020, Art. no. 103500. DOI: https://doi.org/10.1016/j.jbi.2020.103500
A. Montejo-Ráez, M. D. Molina-González, S. M. Jiménez-Zafra, M. Á. García-Cumbreras, and L. J. García-López, ''A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges,'' Computer Science Review, vol. 53, Aug. 2024, Art. no. 100654. DOI: https://doi.org/10.1016/j.cosrev.2024.100654
"South Indian languages social Media Posts Dataset · Issue #1." GitHub, [Online]. Available: https://github.com/melwin-boop/South-indian-Language-social-Media-posts-/issues/1.
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Copyright (c) 2026 K. Alakananda, Ananth G. Prabhu, K. M. Chaitra, Mustafa Basthikodi, Melwin D. Souza
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