Um Modelo Computacional para Análise de Depressão em Dados de Redes Sociais
Resumo
Os transtornos mentais prejudicam a vida de milhões, comprometem a qualidade de vida e reduzem a produtividade. A depressão impacta cerca de 5% dos adultos em todo o mundo e gera custos anuais próximos a US$1 trilhão. Propomos o Serapis, um modelo computacional que organiza dados de redes sociais, questionários e do PHQ-9 para produzir inteligência acionável e apoiar o diagnóstico da depressão. O modelo integra LLMs com uma ontologia construída segundo as melhores práticas de computação forense e OSINT. Desenvolvemos um protótipo MVP que constrói uma linha do tempo de postagens e destaca entradas relevantes para psicólogos. Um grupo focal com profissionais validou a usabilidade do sistema, apontando alta facilidade de uso e utilidade, e recomendou melhorias na acurácia contextual e na colaboração.Referências
Allen, S. (2020). Artificial intelligence and the future of psychiatry. IEEE Pulse, 11(3):2–6.
APA (2022). Diagnostic And Statistical Manual Of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR). [link].
Böhm, I. and Lolagar, S. (2021). Open source intelligence: Introduction, legal, and ethical considerations. International Cybersecurity Law Review, 2(2):317–337.
Chen, L., Magdy, W., Whalley, H., and Wolters, M. K. (2020). Examining the Role of Mood Patterns in Predicting Self-Reported Depressive Symptoms, page 164–173. Association for Computing Machinery, New York, NY, USA.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3):319–339.
Ebner-Priemer, U. W., Mühlbauer, E., Neubauer, A. B., Hill, H., Beier, F., Santangelo, P. S., Ritter, P., Kleindienst, N., Bauer, M., Schmiedek, F., and Severus, E. (2020). Digital phenotyping: towards replicable findings with comprehensive assessments and integrative models in bipolar disorders. International Journal of Bipolar Disorders, 8(1).
Heckler, W. F., Carvalho, J. V. d., and Barbosa, J. L. V. (2022). Machine learning for suicidal ideation identification: A systematic literature review. Computers in Human Behavior, 132:107095.
Heckler, W. F., Feijó, L. P., Carvalho, J. V., and Barbosa, J. L. V. (2023). Thoth: An intelligent model for assisting individuals with suicidal ideation. Expert Systems with Applications, 233:120918.
Islam, M. R., Kabir, M. A., Ahmed, A., Kamal, A. R. M., Wang, H., and Ulhaq, A. (2018). Depression detection from social network data using machine learning techniques. Health Inf. Sci. Syst., 6(1):8.
Jain, V., Chandel, D., Garg, P., and Vishwakarma, D. K. (2020). Depression and impaired mental health analysis from social media platforms using predictive modelling techniques. In 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pages 855–860.
Larsen, O. H. (2022). Open source intelligence techniques: A quantitative study of the norwegian police university college students use of osint techniques in their online investigations through their year of practical training.
Manikonda, L. and De Choudhury, M. (2017). Modeling and understanding visual attributes of mental health disclosures in social media. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI ’17, page 170–181, New York, NY, USA. Association for Computing Machinery.
Marangunić, N. and Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal Access in the Information Society, 14(1):81–95.
Ricard, B. J., Marsch, L. A., Crosier, B., and Hassanpour, S. (2018). Exploring the utility of community-generated social media content for detecting depression: An analytical study on instagram. J Med Internet Res, 20(12):e11817.
SAP (2007). Standardized Technical Architecture Modeling: conceptual and design level.
Schaurer, F. and Störger, J. (2013). The evolution of open source intelligence (osint). Comput Hum Behav, 19:53–56.
Seabrook, E. M., Kern, M. L., Fulcher, B. D., and Rickard, N. S. (2018). Predicting depression from language-based emotion dynamics: Longitudinal analysis of facebook and twitter status updates. J Med Internet Res, 20(5):e168.
WHO (2022). World mental health report: Transforming mental health for all - executive summary. [link]. World Health Organization.
Wongkoblap, A., Vadillo, M. A., and Curcin, V. (2019). Predicting social network users with depression from simulated temporal data. In IEEE EUROCON 2019 -18th International Conference on Smart Technologies, pages 1–6.
Yang, X., McEwen, R., Ong, L. R., and Zihayat, M. (2020). A big data analytics framework for detecting user-level depression from social networks. International Journal of Information Management, 54:102141.
APA (2022). Diagnostic And Statistical Manual Of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR). [link].
Böhm, I. and Lolagar, S. (2021). Open source intelligence: Introduction, legal, and ethical considerations. International Cybersecurity Law Review, 2(2):317–337.
Chen, L., Magdy, W., Whalley, H., and Wolters, M. K. (2020). Examining the Role of Mood Patterns in Predicting Self-Reported Depressive Symptoms, page 164–173. Association for Computing Machinery, New York, NY, USA.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3):319–339.
Ebner-Priemer, U. W., Mühlbauer, E., Neubauer, A. B., Hill, H., Beier, F., Santangelo, P. S., Ritter, P., Kleindienst, N., Bauer, M., Schmiedek, F., and Severus, E. (2020). Digital phenotyping: towards replicable findings with comprehensive assessments and integrative models in bipolar disorders. International Journal of Bipolar Disorders, 8(1).
Heckler, W. F., Carvalho, J. V. d., and Barbosa, J. L. V. (2022). Machine learning for suicidal ideation identification: A systematic literature review. Computers in Human Behavior, 132:107095.
Heckler, W. F., Feijó, L. P., Carvalho, J. V., and Barbosa, J. L. V. (2023). Thoth: An intelligent model for assisting individuals with suicidal ideation. Expert Systems with Applications, 233:120918.
Islam, M. R., Kabir, M. A., Ahmed, A., Kamal, A. R. M., Wang, H., and Ulhaq, A. (2018). Depression detection from social network data using machine learning techniques. Health Inf. Sci. Syst., 6(1):8.
Jain, V., Chandel, D., Garg, P., and Vishwakarma, D. K. (2020). Depression and impaired mental health analysis from social media platforms using predictive modelling techniques. In 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pages 855–860.
Larsen, O. H. (2022). Open source intelligence techniques: A quantitative study of the norwegian police university college students use of osint techniques in their online investigations through their year of practical training.
Manikonda, L. and De Choudhury, M. (2017). Modeling and understanding visual attributes of mental health disclosures in social media. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI ’17, page 170–181, New York, NY, USA. Association for Computing Machinery.
Marangunić, N. and Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal Access in the Information Society, 14(1):81–95.
Ricard, B. J., Marsch, L. A., Crosier, B., and Hassanpour, S. (2018). Exploring the utility of community-generated social media content for detecting depression: An analytical study on instagram. J Med Internet Res, 20(12):e11817.
SAP (2007). Standardized Technical Architecture Modeling: conceptual and design level.
Schaurer, F. and Störger, J. (2013). The evolution of open source intelligence (osint). Comput Hum Behav, 19:53–56.
Seabrook, E. M., Kern, M. L., Fulcher, B. D., and Rickard, N. S. (2018). Predicting depression from language-based emotion dynamics: Longitudinal analysis of facebook and twitter status updates. J Med Internet Res, 20(5):e168.
WHO (2022). World mental health report: Transforming mental health for all - executive summary. [link]. World Health Organization.
Wongkoblap, A., Vadillo, M. A., and Curcin, V. (2019). Predicting social network users with depression from simulated temporal data. In IEEE EUROCON 2019 -18th International Conference on Smart Technologies, pages 1–6.
Yang, X., McEwen, R., Ong, L. R., and Zihayat, M. (2020). A big data analytics framework for detecting user-level depression from social networks. International Journal of Information Management, 54:102141.
Publicado
09/06/2025
Como Citar
FOPPA, Alexandre Augusto; BARBOSA, Jorge Luis Vitória.
Um Modelo Computacional para Análise de Depressão em Dados de Redes Sociais. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 25. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 92-103.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2025.6939.
