Big Data Applications in Online Psychological Counseling: A Systematic Literature Review
DOI:
https://doi.org/10.56127/ijml.v5i2.2888Keywords:
Big Data; Online Psychological Counseling; Digital Mental Health; Machine Learning; Natural Language ProcessingAbstract
The development of digital technology has transformed mental health services, including online psychological counseling. The increasing use of digital platforms generates large volumes of psychological and behavioral data that can be analyzed through Big Data approaches to support assessment, intervention, monitoring, and decision-making processes. This study aims to systematically review the application of Big Data in online psychological counseling by examining its implementation approaches, supporting technologies, benefits, challenges, and future research directions. A Systematic Literature Review (SLR) was conducted following PRISMA 2020 guidelines. Literature searches were performed in Scopus, ScienceDirect, PubMed, SpringerLink, and Google Scholar, resulting in 20 eligible studies published between 2016 and 2026 for qualitative synthesis. The findings indicate that Big Data has been applied in data integration, behavioral analysis, counseling conversation analysis, clinical decision support, and digital intervention development. Key enabling technologies include Natural Language Processing, Machine Learning, social media analytics, conversational agents, and Large Language Models. The reviewed studies highlight several benefits, including personalized mental health services, early identification of mental health risks, improved accessibility, and enhanced scalability of counseling support. However, important challenges remain, particularly regarding data privacy, informed consent, algorithmic bias, transparency, fairness, and the generalizability of computational models. Future research should focus on integrating heterogeneous data sources, developing explainable artificial intelligence systems, strengthening ethical governance frameworks, and evaluating long-term effectiveness in real-world counseling settings. Overall, Big Data demonstrates considerable potential to support more effective, ethical, and evidence-based digital mental health services.
References
Abd-Alrazaq, A. A., Rababeh, A., Alajlani, M., Bewick, B. M., & Househ, M. (2020). Effectiveness and safety of using chatbots to improve mental health: Systematic review and meta-analysis. Journal of Medical Internet Research, 22(7), e16021. https://doi.org/10.2196/16021
Ahmed, A., Agus, M., Alzubaidi, M., Aziz, S., Abd-Alrazaq, A., Giannicchi, A., & Househ, M. (2022). Overview of the role of big data in mental health: A scoping review. Computer Methods and Programs in Biomedicine Update, 2, Article 100076. https://doi.org/10.1016/j.cmpbup.2022.100076
Althoff, T., Clark, K., & Leskovec, J. (2016). Large-scale analysis of counseling conversations: An application of natural language processing to mental health. Transactions of the Association for Computational Linguistics, 4, 463–476. https://doi.org/10.1162/tacl_a_00111
Berardi, C., Antonini, M., Jordan, Z., Wechtler, H., Paolucci, F., & Hinwood, M. (2024). Barriers and facilitators to the implementation of digital technologies in mental health systems: A qualitative systematic review to inform a policy framework. BMC Health Services Research, 24, 243. https://doi.org/10.1186/s12913-023-10536-1
Cho H, N., Wang J., Hu D., Zheng K. (2026). Large Language Model–Based Chatbots and Agentic AI for Mental Health Counseling: Systematic Review of Methodologies, Evaluation Frameworks, and Ethical Safeguards. JMIR AI, 5, e80348. https://doi.org/10.2196/80348
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4171–4186. https://doi.org/10.18653/v1/N19-1423
Eichstaedt, J. C., Smith, R. J., Merchant, R. M., Ungar, L. H., Foxwell, J., Crutchley, P., & Schwartz, H. A. (2018). Facebook language predicts depression. Proceedings of the National Academy of Sciences, 115(44), 11203–11208. https://doi.org/10.1073/pnas.1802331115
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent. JMIR Mental Health, 4(2), e19. https://doi.org/10.2196/mental.7785
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28, 689–707. https://doi.org/10.1007/s11023-018-9482-5
Gooding, P., Kariotis, T. (2021). Ethics and law in research on algorithmic and data-driven technology in mental health care: Scoping review. Journal of Medical Internet Research, 23(6), e24668. https://doi.org/10.2196/24668
Guo, Z., Lai, A. M., & Li, K. (2024). Large language models in mental health care: Opportunities and risks. JMIR Mental Health, 11, e57400. doi:10.2196/57400
Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017). Detecting depression and mental illness on social media: An integrative review. Current Opinion in Behavioral Sciences, 18, 43–49. https://doi.org/10.1016/j.cobeha.2017.07.005
Hua, Y., Liu, F., Yang, K., Li, Z., Na, H., Sheu, Y.-H., Zhou, P., Moran, L. V., Ananiadou, S., Beam, A., & Torous, J. (2024). Large language models in mental health care: A scoping review. arXiv. https://arxiv.org/abs/2401.02984
Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., ... Coiera, E. (2018). Conversational agents in healthcare: A systematic review. Journal of Medical Internet Research, 20(5), e101. https://doi.org/10.2196/jmir.8231
Le Glaz, A., Haralambous, Y., Kim-Dufor, D. H., Lenca, P., Billot, R., Ryan, T. C., Marsh, J., DeVylder, J., Walter, M., Berrouiguet, S., & Lemey, C. (2021). Machine learning and natural language processing in mental health: Systematic review. Journal of Medical Internet Research, 23(5), e15708. https://doi.org/10.2196/15708
Malgaroli, M., Hull, T. D., & Zech, J. M. (2023). Natural language processing for mental health interventions: A systematic review. Translational Psychiatry, 13, 309. https://doi.org/10.1038/s41398-023-02592-2
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Philippe, T. J., Sikder, N., Jackson, A., Koblanski, M. E., Liow, E., Pilarinos, A., Vasarhelyi, K. (2022). Digital health interventions for delivery of mental health care: Systematic and comprehensive meta-review. JMIR Mental Health, 9(5), e35159. https://doi.org/10.2196/35159
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine: Opportunities and challenges for clinical deployment. Nature Medicine, 28, 31–38. https://doi.org/10.1038/s41591-021-01614-0
Reece, A. G., & Danforth, C. M. (2017). Instagram photos reveal predictive markers of depression. EPJ Data Science, 6, Article 15. https://doi.org/10.1140/epjds/s13688-017-0110-z
Schindler, M., & Domahidi, E. (2023). The computational turn in online mental health research: A systematic review. New Media & Society, 25(10), 2781–2799. https://doi.org/10.1177/14614448221122212
Shaw, J., & Sekalala, S. (2023). Health data justice: Building new norms for health data governance. npj Digital Medicine, 6, 30. https://doi.org/10.1038/s41746-023-00780-4
Shen, G., Jia, J., Nie, L., Feng, F., Zhang, C., Hu, T., Chua, T.-S., & Zhu, W. (2017). Depression detection via harvesting social media: A multimodal dictionary learning solution. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) (pp. 3838–3844). International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2017/536
Stewart, R., & Davis, K. (2016). Big data in mental health research: Current Status and Emerging Possibilities. Social Psychiatry and Psychiatric Epidemiology, 51(8), 1055–1072. https://doi.org/10.1007/s00127-016-1266-8
Vaidyam, A. N., Wisniewski, H., Halamka, J. D., Kashavan, M. S., & Torous, J. B. (2019). Chatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape. The Canadian Journal of Psychiatry, 64(7), 456–464. https://doi.org/10.1177/0706743719828977
Downloads
Published
Issue
Section
Citation Check
License
Copyright (c) 2026 Tissa Maharani
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
