MMDRS - Metodologia para Monitoramento de Depressão em Redes Sociais
Resumo
Este trabalho propõe uma metodologia para detecção e intervenção nos casos de manifestações depressivas em redes sociais, utilizando Computação Afetiva e técnicas de Aprendizagem de Máquina. Baseado nessa metodologia, desenvolveu-se o protótipo SaveMeNow, que pode monitorar plataformas como Facebook e Twitter, classificando mensagens potencialmente depressivas e direcionando-as para intervenção profissional. O sistema é dividido em dois módulos principais: Monitoramento e Atendimento, detalhados com suas respectivas tecnologias e processos implementados. A proposta visa agilizar a identificação de sinais de depressão em redes sociais e proporcionar intervenções rápidas e eficazes, contribuindo para a redução das taxas de suicídio.
Palavras-chave:
Monitoramento de Depressão, Redes Sociais, Computação Afetiva, Aprendizagem de Máquina
Referências
Barros, J., Morales, S., Echávarri, O., García, A., Ortega, J., Asahi, T., Moya, C., Fischman, R., Maino, M. P., & Núñez, C. (2017). Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders. Brazilian Journal of Psychiatry, 39(1), 1-11. SciELO Brasil.
Calderon-Vilca, H. D., Wun-Rafael, W. I., & Miranda-Loarte, R. (2017). Simulation of suicide tendency by using machine learning. In 2017 36th International Conference of the Chilean Computer Science Society (SCCC) (pp. 1-6). IEEE. DOI: 10.1109/SCCC.2017.8405128,
Caplan, S. E. (2003). Preference for Online Social Interaction: A Theory of Problematic Internet Use and Psychosocial Well-Being. Communication Research, 30(6), 625-648. DOI: 10.1177/0093650203257842,
Carvalho, F., Junior, F. P., Ogasawara, E., Ferrari, L., & Guedes, G. (2024). Evaluation of the Brazilian Portuguese version of Linguistic Inquiry and Word Count 2015 (BP-LIWC2015). Language Resources and Evaluation, 58(1), 203-222. Springer.
CID10 (1992). Classificação Estatística Internacional de Doenças e Problemas Relacionados à Saúde - CID-10. Organização Mundial da Saúde. Disponível em: [link]. Acesso em: 25 jul. 2024.
Cortes, O. A. C., & de Oliveira Melo, W. E. (2021). Utilizando Análise de Sentimentos e SVM na Classificação de Tweets Depressivos. Anais do Computer on the Beach, 12, 102-110.
da Silva Nascimento, R., Parreira, P., dos Santos, G. N., & Guedes, G. P. (2018). Identificando Sinais de Comportamento Depressivo em Redes Sociais. In Anais do VII Brazilian Workshop on Social Network Analysis and Mining. SBC.
Desmet, B., & Hoste, V. (2014). Recognising suicidal messages in Dutch social media. In 9th international conference on language resources and evaluation (LREC) (pp. 830-835).
Ernst, M., Kallenbach-Kaminski, L., Kaufhold, J., Negele, A., Bahrke, U., Hautzinger, M., Beutel, M. E., & Leuzinger-Bohleber, M. (2019). Suicide Attempts in Chronically Depressed Individuals: What Are the Risk Factors? Psychiatry Research, 112481. Elsevier.
Filho, S. L., Silva, E., Oliveira, J., & Silva, M. (2024). DepressSet: Um conjunto de dados de análises textuais sobre postagens depressivas. Anais do XIII Brazilian Workshop on Social Network Analysis and Mining, 214-220. SBC. DOI: 10.5753/brasnam.2024.2774,
Glenn, C. R., Kleiman, E. M., Kellerman, J., Pollak, O., Cha, C. B., Esposito, E. C., Porter, A. C., Wyman, P. A., & Boatman, A. E. (2020). Annual Research Review: A meta-analytic review of worldwide suicide rates in adolescents. Journal of Child Psychology and Psychiatry, 61(3), 294-308. Wiley Online Library.
Ivanich, J. D., O’Keefe, V., Waugh, E., Tingey, L., Tate, M., Parker, A., Craig, M., & Cwik, M. (2021). Social network differences between American Indian youth who have attempted suicide and have suicide ideation. Community Mental Health Journal. Springer.
Nascimento, R., Carvalho, F., & Guedes, G. (2019). Identificando sintomas depressivos: um estudo de caso no YouTube. In Anais do VIII Brazilian Workshop on Social Network Analysis and Mining (pp. 119-130). SBC.
Park, M., Cha, C., & Cha, M. (2012). Depressive moods of users portrayed in Twitter. In Proceedings of the ACM SIGKDD Workshop on Healthcare Informatics (HI-KDD) (pp. 1–8). New York, NY, USA: ACM.
Parraga-Alava, J., Caicedo, R. A., Gómez, J. M., & Inostroza-Ponta, M. (2019). An unsupervised learning approach to automatically categorize potential suicide messages in social media. In 2019 38th International Conference of the Chilean Computer Science Society (SCCC) (pp. 1-8). IEEE.
Picard, R. W. (2000). Affective computing. MIT Press, Cambridge, MA.
Santos, W. R. d., de Oliveira, R. L., and Paraboni, I. (2024). SetembroBR: a social media corpus for depression and anxiety disorder prediction. Language Resources and Evaluation, 58(1):273–300.
Sharma, M., Pant, B., Singh, V., and Kumar, S. (2021). STP: Suicidal Tendency Prediction among the youth using social network data. In Next Generation Information Processing System, pages 161–169. Springer.
Simon, G. E., Stewart, C. C., Gary, M. C., and Richards, J. E. (2021). Detecting and assessing suicide ideation during the COVID-19 pandemic. The Joint Commission Journal on Quality and Patient Safety, 47(7):452–457.
Varathan, K. D. and Talib, N. (2014). Suicide detection system based on Twitter. In 2014 Science and Information Conference, pages 785–788. IEEE.
WHO (2017a). Depression and other common mental disorders: global health estimates.
WHO (2017b). Depression Let’s talk. Campaing Essential.
Winkler, W. E. (1999). The state of record linkage and current research problems. Technical report, Statistical Research Division, U.S. Bureau of the Census.
Calderon-Vilca, H. D., Wun-Rafael, W. I., & Miranda-Loarte, R. (2017). Simulation of suicide tendency by using machine learning. In 2017 36th International Conference of the Chilean Computer Science Society (SCCC) (pp. 1-6). IEEE. DOI: 10.1109/SCCC.2017.8405128,
Caplan, S. E. (2003). Preference for Online Social Interaction: A Theory of Problematic Internet Use and Psychosocial Well-Being. Communication Research, 30(6), 625-648. DOI: 10.1177/0093650203257842,
Carvalho, F., Junior, F. P., Ogasawara, E., Ferrari, L., & Guedes, G. (2024). Evaluation of the Brazilian Portuguese version of Linguistic Inquiry and Word Count 2015 (BP-LIWC2015). Language Resources and Evaluation, 58(1), 203-222. Springer.
CID10 (1992). Classificação Estatística Internacional de Doenças e Problemas Relacionados à Saúde - CID-10. Organização Mundial da Saúde. Disponível em: [link]. Acesso em: 25 jul. 2024.
Cortes, O. A. C., & de Oliveira Melo, W. E. (2021). Utilizando Análise de Sentimentos e SVM na Classificação de Tweets Depressivos. Anais do Computer on the Beach, 12, 102-110.
da Silva Nascimento, R., Parreira, P., dos Santos, G. N., & Guedes, G. P. (2018). Identificando Sinais de Comportamento Depressivo em Redes Sociais. In Anais do VII Brazilian Workshop on Social Network Analysis and Mining. SBC.
Desmet, B., & Hoste, V. (2014). Recognising suicidal messages in Dutch social media. In 9th international conference on language resources and evaluation (LREC) (pp. 830-835).
Ernst, M., Kallenbach-Kaminski, L., Kaufhold, J., Negele, A., Bahrke, U., Hautzinger, M., Beutel, M. E., & Leuzinger-Bohleber, M. (2019). Suicide Attempts in Chronically Depressed Individuals: What Are the Risk Factors? Psychiatry Research, 112481. Elsevier.
Filho, S. L., Silva, E., Oliveira, J., & Silva, M. (2024). DepressSet: Um conjunto de dados de análises textuais sobre postagens depressivas. Anais do XIII Brazilian Workshop on Social Network Analysis and Mining, 214-220. SBC. DOI: 10.5753/brasnam.2024.2774,
Glenn, C. R., Kleiman, E. M., Kellerman, J., Pollak, O., Cha, C. B., Esposito, E. C., Porter, A. C., Wyman, P. A., & Boatman, A. E. (2020). Annual Research Review: A meta-analytic review of worldwide suicide rates in adolescents. Journal of Child Psychology and Psychiatry, 61(3), 294-308. Wiley Online Library.
Ivanich, J. D., O’Keefe, V., Waugh, E., Tingey, L., Tate, M., Parker, A., Craig, M., & Cwik, M. (2021). Social network differences between American Indian youth who have attempted suicide and have suicide ideation. Community Mental Health Journal. Springer.
Nascimento, R., Carvalho, F., & Guedes, G. (2019). Identificando sintomas depressivos: um estudo de caso no YouTube. In Anais do VIII Brazilian Workshop on Social Network Analysis and Mining (pp. 119-130). SBC.
Park, M., Cha, C., & Cha, M. (2012). Depressive moods of users portrayed in Twitter. In Proceedings of the ACM SIGKDD Workshop on Healthcare Informatics (HI-KDD) (pp. 1–8). New York, NY, USA: ACM.
Parraga-Alava, J., Caicedo, R. A., Gómez, J. M., & Inostroza-Ponta, M. (2019). An unsupervised learning approach to automatically categorize potential suicide messages in social media. In 2019 38th International Conference of the Chilean Computer Science Society (SCCC) (pp. 1-8). IEEE.
Picard, R. W. (2000). Affective computing. MIT Press, Cambridge, MA.
Santos, W. R. d., de Oliveira, R. L., and Paraboni, I. (2024). SetembroBR: a social media corpus for depression and anxiety disorder prediction. Language Resources and Evaluation, 58(1):273–300.
Sharma, M., Pant, B., Singh, V., and Kumar, S. (2021). STP: Suicidal Tendency Prediction among the youth using social network data. In Next Generation Information Processing System, pages 161–169. Springer.
Simon, G. E., Stewart, C. C., Gary, M. C., and Richards, J. E. (2021). Detecting and assessing suicide ideation during the COVID-19 pandemic. The Joint Commission Journal on Quality and Patient Safety, 47(7):452–457.
Varathan, K. D. and Talib, N. (2014). Suicide detection system based on Twitter. In 2014 Science and Information Conference, pages 785–788. IEEE.
WHO (2017a). Depression and other common mental disorders: global health estimates.
WHO (2017b). Depression Let’s talk. Campaing Essential.
Winkler, W. E. (1999). The state of record linkage and current research problems. Technical report, Statistical Research Division, U.S. Bureau of the Census.
Publicado
14/10/2024
Como Citar
CUNHA, Alexandre M. da; NASCIMENTO, Rodolpho da S.; CARVALHO, Flavio; GUEDES, Gustavo.
MMDRS - Metodologia para Monitoramento de Depressão em Redes Sociais. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 18. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 88-95.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2024.244247.
