A system for monitoring depression symptoms from posts on social networks
Resumo
Este trabalho apresenta o desenvolvimento de um sistema para monitoramento de sintomas depressivos a partir de publicações em redes sociais, com foco na plataforma Reddit. Por meio da combinação de técnicas de Processamento de Linguagem Natural e modelos de aprendizado de máquina, as publicações foram classificadas quanto ao potencial indício de depressão, sendo complementadas por análises léxicas e temáticas. A aplicação web desenvolvida permite visualização estatística e monitoramento em tempo real, com funcionalidades voltadas ao apoio de profissionais de saúde mental. Os resultados obtidos evidenciam uma correlação significativa entre o conteúdo das comunidades analisadas e a presença de sinais depressivos, sugerindo o potencial do sistema como ferramenta auxiliar na triagem precoce de sintomas. Questões éticas relacionadas à privacidade e à acurácia das classificações também são discutidas.Referências
American Psychiatric Association, A., Association, A. P., et al. (2013). Diagnostic and statistical manual of mental disorders: DSM-5, volume 10. Washington, DC: American psychiatric association.
Angskun, J., Tipprasert, S., and Angskun, T. (2022). Big data analytics on social networks for real-time depression detection. Journal of Big Data, 9(1):69.
Del Vicario, M., Vivaldo, G., Bessi, A., Zollo, F., Scala, A., Caldarelli, G., Stanley, H. E., and Quattrociocchi, W. (2016). Echo chambers: Emotional contagion and group polarization on facebook. Scientific Reports, 6:37825.
Fan, R., Xu, K., and Zhao, J. (2016). Higher contagion and weaker ties mean anger spreads faster than joy in social media. arXiv preprint arXiv:1608.03656.
Fast, E., Chen, B., and Bernstein, M. S. (2016). Empath: Understanding topic signals in large-scale text. In Proceedings of the 2016 CHI conference on human factors in computing systems, pages 4647–4657.
Fazel, S., Wolf, A., Chang, Z., Larsson, H., Goodwin, G. M., and Lichtenstein, P. (2015). Depression and violence: a swedish population study. The Lancet Psychiatry, 2(3):224–232.
Force, U. P. S. T. (2023). Screening for depression and suicide risk in adults: Us preventive services task force recommendation statement. JAMA, 329(23):2057–2067.
Ghassemi, M., Naumann, T., Schulam, P., Beam, A. L., Chen, I. Y., and Ranganath, R. (2020). A review of challenges and opportunities in machine learning for health. AMIA Summits on Translational Science Proceedings, 2020:191.
Hutto, C. and Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1):216–225.
Martínez-Castaño, R., Pichel, J. C., and Losada , D. E. (2020). A big data platform for real time analysis of signs of depression in social media. International Journal of Environmental Research and Public Health, 17(13).
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Comput. Surv., 54(6):1–35.
Morley, J., Machado, C. C., Burr, C., Cowls, J., Joshi, I., Taddeo, M., and Floridi, L. (2020). The ethics of ai in health care: a mapping review. Soc Sci Med, 260:113172.
Naseem, U., Dunn, A. G., Kim, J., and Khushi, M. (2022). Early identification of depression severity levels on reddit using ordinal classification. In Proceedings of the ACM Web Conference 2022, WWW ’22, page 2563–2572, New York, NY, USA. Association for Computing Machinery.
National Institute of Mental Health (NIMH) (2024). Depression. [link]. Accessed: 2025-06-04.
OPAS (2022). Depressão - opas/oms — organização pan-americana da saúde.
Pennebaker, J., Francis, M., and Booth, R. (2001). Linguistic inquiry and word count (liwc): Liwc2001. 71.
Rosa, R. L., Rodríguez, D. Z., Schwartz, G. M., de Campos Ribeiro, I., and Bressan, G. (2016). Monitoring system for potential users with depression using sentiment analysis. In 2016 IEEE International Conference on Consumer Electronics (ICCE), pages 381–382.
Seabrook, E. M., Kern, M. L., and Rickard, N. S. (2016). Social networking sites, depression, and anxiety: a systematic review. JMIR mental health, 3(4):e5842.
Tadesse, M. M., Lin, H., Xu, B., and Yang, L. (2019). Detection of depression-related posts in reddit social media forum. IEEE Access, 7:44883–44893.
Vedula, N. and Parthasarathy, S. (2017). Emotional and linguistic cues of depression from social media. In Proceedings of the 2017 International Conference on Digital Health, DH ’17, page 127–136, New York, NY, USA. Association for Computing Machinery.
Wang, Y., Hu, W., Zhou, K., et al. (2025). What is the role of human decisions in a world of artificial intelligence: an economic evaluation of human-ai collaboration in diabetic retinopathy screening. ArXiv, abs/2503.20160.
World Health Organization (2017). Depression and other common mental disorders: Global health estimates. Technical report, World Health Organization, Geneva. Licence: CC BY-NC-SA 3.0 IGO.
World Health Organization (2023). Depression. [link]. Accessed: 2025-05-21.
Yazdavar, A. H., Al-Olimat, H. S., Ebrahimi, M., Bajaj, G., Banerjee, T., Thirunarayan, K., Pathak, J., and Sheth, A. (2017). Semi-supervised approach to monitoring clinical depressive symptoms in social media. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, ASONAM ’17, page 1191–1198, New York, NY, USA. Association for Computing Machinery.
Yoon, S., Kleinman, M., Mertz, J., and Brannick, M. (2019). Is social network site usage related to depression? a meta-analysis of facebook–depression relations. Journal of Affective Disorders, 248:65–72.
Angskun, J., Tipprasert, S., and Angskun, T. (2022). Big data analytics on social networks for real-time depression detection. Journal of Big Data, 9(1):69.
Del Vicario, M., Vivaldo, G., Bessi, A., Zollo, F., Scala, A., Caldarelli, G., Stanley, H. E., and Quattrociocchi, W. (2016). Echo chambers: Emotional contagion and group polarization on facebook. Scientific Reports, 6:37825.
Fan, R., Xu, K., and Zhao, J. (2016). Higher contagion and weaker ties mean anger spreads faster than joy in social media. arXiv preprint arXiv:1608.03656.
Fast, E., Chen, B., and Bernstein, M. S. (2016). Empath: Understanding topic signals in large-scale text. In Proceedings of the 2016 CHI conference on human factors in computing systems, pages 4647–4657.
Fazel, S., Wolf, A., Chang, Z., Larsson, H., Goodwin, G. M., and Lichtenstein, P. (2015). Depression and violence: a swedish population study. The Lancet Psychiatry, 2(3):224–232.
Force, U. P. S. T. (2023). Screening for depression and suicide risk in adults: Us preventive services task force recommendation statement. JAMA, 329(23):2057–2067.
Ghassemi, M., Naumann, T., Schulam, P., Beam, A. L., Chen, I. Y., and Ranganath, R. (2020). A review of challenges and opportunities in machine learning for health. AMIA Summits on Translational Science Proceedings, 2020:191.
Hutto, C. and Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1):216–225.
Martínez-Castaño, R., Pichel, J. C., and Losada , D. E. (2020). A big data platform for real time analysis of signs of depression in social media. International Journal of Environmental Research and Public Health, 17(13).
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Comput. Surv., 54(6):1–35.
Morley, J., Machado, C. C., Burr, C., Cowls, J., Joshi, I., Taddeo, M., and Floridi, L. (2020). The ethics of ai in health care: a mapping review. Soc Sci Med, 260:113172.
Naseem, U., Dunn, A. G., Kim, J., and Khushi, M. (2022). Early identification of depression severity levels on reddit using ordinal classification. In Proceedings of the ACM Web Conference 2022, WWW ’22, page 2563–2572, New York, NY, USA. Association for Computing Machinery.
National Institute of Mental Health (NIMH) (2024). Depression. [link]. Accessed: 2025-06-04.
OPAS (2022). Depressão - opas/oms — organização pan-americana da saúde.
Pennebaker, J., Francis, M., and Booth, R. (2001). Linguistic inquiry and word count (liwc): Liwc2001. 71.
Rosa, R. L., Rodríguez, D. Z., Schwartz, G. M., de Campos Ribeiro, I., and Bressan, G. (2016). Monitoring system for potential users with depression using sentiment analysis. In 2016 IEEE International Conference on Consumer Electronics (ICCE), pages 381–382.
Seabrook, E. M., Kern, M. L., and Rickard, N. S. (2016). Social networking sites, depression, and anxiety: a systematic review. JMIR mental health, 3(4):e5842.
Tadesse, M. M., Lin, H., Xu, B., and Yang, L. (2019). Detection of depression-related posts in reddit social media forum. IEEE Access, 7:44883–44893.
Vedula, N. and Parthasarathy, S. (2017). Emotional and linguistic cues of depression from social media. In Proceedings of the 2017 International Conference on Digital Health, DH ’17, page 127–136, New York, NY, USA. Association for Computing Machinery.
Wang, Y., Hu, W., Zhou, K., et al. (2025). What is the role of human decisions in a world of artificial intelligence: an economic evaluation of human-ai collaboration in diabetic retinopathy screening. ArXiv, abs/2503.20160.
World Health Organization (2017). Depression and other common mental disorders: Global health estimates. Technical report, World Health Organization, Geneva. Licence: CC BY-NC-SA 3.0 IGO.
World Health Organization (2023). Depression. [link]. Accessed: 2025-05-21.
Yazdavar, A. H., Al-Olimat, H. S., Ebrahimi, M., Bajaj, G., Banerjee, T., Thirunarayan, K., Pathak, J., and Sheth, A. (2017). Semi-supervised approach to monitoring clinical depressive symptoms in social media. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, ASONAM ’17, page 1191–1198, New York, NY, USA. Association for Computing Machinery.
Yoon, S., Kleinman, M., Mertz, J., and Brannick, M. (2019). Is social network site usage related to depression? a meta-analysis of facebook–depression relations. Journal of Affective Disorders, 248:65–72.
Publicado
29/09/2025
Como Citar
CARVALHO, Rodrigo; UTINO, Matheus Yasuo Ribeiro; MANN, Paulo; MONTEIRO, Rodrigo Salvador.
A system for monitoring depression symptoms from posts on social networks. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 22. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 213-224.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2025.12291.
