SOCIETY & ”INDUSTRY 4.0”
Using Large Language Models for Emotional Support of Bulgarian Users: A Survey
- 1 Faculty of Mathematics and Informatics - Sofia University "St. Kliment Ohridski", Bulgaria
Abstract
The use of large language models (LLMs) for psychological and emotional support (ES) has rapidly evolved, becoming the most widely used application of generative artificial intelligence among consumers by 2025. This paper presents the results of an anonymous survey of 100 Bulgarian users, primarily high school, university, and doctoral students, to explore their attitudes toward and usage of chatbots for emotional support. Findings indicate that approximately one-half of the surveyed population utilizes chatbots for ES, with ChatGPT being the most dominant platform. Users primarily seek support for coping with stress in interpersonal relationships and work or study-related environments. While 71% of users perceive the technology as effective, non-users remain sceptical. Despite the growing adoption, significant concerns persist regarding data security, technology reliability, and the tendency of chatbots to provide excessive affirmation.
Keywords
References
- M. Zao-Sanders. How People Are Really Using Gen AI in 2025. Apr. 9, 2025. url: https://hbr.org/2025/04/how-people-are-really-using-gen-ai-in-2025 (visited on 05/03/2026).
- B. R. Burleson. Emotional support skills. Lawrence Erlbaum Associates Publishers, 2003.
- L. Brocki, G. C. Dyer, A. G ladka, and N. C. Chung. ―Deep Learning Mental Health Dialogue System.‖ In: 2023 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE. 2023, pp. 395–398.
- World Health Organization. Mental Health Atlas 2020. Geneva, October 8, 2021. url: https://www.who.int/publications/i/item/9789240036703 (visited on 05/03/2026).
- D. F. Santomauro, A. M. M. Herrera, J. Shadid, P. Zheng, C. Ashbaugh, D. M. Pigott,C. Abbafati, C. Adolph, J. O. Amlag, A. Y. Aravkin, et al. ―Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic.‖ In: The Lancet 398.10312 (2021), pp. 1700– 1712.
- G. M. Lucas, J. Gratch, A. King, and L.-P. Morency. ―It’s only a computer: Virtual humans increase willingness to disclose.‖ In: Computers in Human Behavior 37 (2014), pp. 94–100.
- D. R. Lehman and K. J. Hemphill. ―Recipients’ perceptions of support attempts and attributions for support attempts that fail‖. In: Journal of Social and Personal Relationships 7.4 (1990), pp. 563– 574.
- M. Baidal, E. Derner, and N. Oliver. ―Guardians of Trust: Risks and Opportunities for LLMs in Mental Health.‖ In: Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI). Vienna, Austria: Association for Computational Linguistics, July 2025, pp. 11–22. isbn: 978-1-959429-19-7. url: https://aclanthology.org/2025.nlp4pi-1.2/ .
- N. Obradovich, S. S. Khalsa, W. U. Khan, J. Suh, R. H. Perlis, O. Ajilore, and M. P. Paulus. ―Opportunities and risks of large language models in psychiatry.‖ In: NPP—Digital Psychiatry and Neuroscience 2.1 (2024), p. 8.
- A. Babu and A. P. Joseph. ―Artificial intelligence in mental healthcare: transformative potential vs. the necessity of human interaction.‖ In: Frontiers in Psychology 15 (2024),p. 1378904.
- Carganilla, Marielle, and John Rey Pelila. "ZILLENIAL MICROGENERATION: HYBRID TRAITS, DIGITAL BEHAVIOR, AND GENERATIONAL BOUNDARIES." Lingue: Jurnal Bahasa, Budaya, dan Sastra 7.2 (2025): 218-227.
- Cheng, Myra, et al. "Social sycophancy: A broader understanding of llm sycophancy." arXiv preprint arXiv:2505.13995 (2025).
- Balint, Alice, and Michael Balint. "On transference and counter-transference." The International Journal of Psycho- Analysis 20 (1939): 223.