Identificando sintomas depressivos: um estudo de caso no Youtube
Resumo
A depressão é uma doença que vem tomando proporções preocupan- tes em todo o mundo. Segundo a Organização Mundial de Saúde, mais de 300 milhões de pessoas sofrem deste mal. Com a popularização e facilidades de acesso à Internet nos dias atuais, é comum encontrar indivı́duos depressivos que, na tentativa de aliviar seus sintomas, acabam expondo seus sentimentos em redes sociais antes de chegar ao extremo do suicı́dio. Este trabalho propõe o uso de um léxico para aprimorar a identificação de sintomas depressivos em redes sociais brasileiras. Experimentos preliminares indicaram que a utilização do léxico melhoram os resultados nas tarefas classificação para detecção de sintomas de depressão em textos.
Palavras-chave:
Depressão, YouTube, Doença mental, Rede social, Text mining
Referências
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Kristensen, C. H., Gomes, C. F. A. d. A., Justo, A. R., and Vieira, K. (2011). Normas brasileiras para o Affective Norms for English Words. Trends in Psychiatry and Psy- chotherapy, 33:135 – 146.
Mate, S. and Curcio, I. (2009). Mobile and interactive social television. IEEE Communi- cations Magazine, 47(12):116–122.
Nascimento, R., Cruz, P., dos Santos, G., and Guedes, G. P. (2018). Identificando sinais de comportamento depressivo em redes sociais. In 7th Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2018), Natal, RN, Brasil. SBC.
Nguyen, T., Phung, D., Dao, B., Venkatesh, S., and Berk, M. (2014). Affective and content analysis of online depression communities. IEEE Transactions on Affective Computing, 5(3):217–226.
Pennebaker, J. W. (2011). The secret life of pronouns: what our words say about us. Bloomsbury Press.
PNAD, I. (2017). Acesso à internet e à televisão e posse de telefone móvel celular para uso pessoal : 2017.
Radloff, L. S. (1977). The CES-D scale: a self-report depression scale for research in the general population. Applied psychological measurement, 1(3):385–401.
Rude, S., Gortner, E.-M., and Pennebaker, J. (2004). Language use of depressed and depression-vulnerable college students. Cognition & Emotion, 18(8):1121–1133.
Silveira, B., da Silva, A. P. C., and Murai, F. (2018). Análise de comunidades de suporte a transtornos de saúde mental do Reddit. In 7th Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2018), Natal, RN, Brasil. SBC.
Tausczik, Y. R. and Pennebaker, J. W. (2009). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psy- chology, 29(1):24–54.
Tsugawa, S., Kikuchi, Y., Kishino, F., Nakajima, K., Itoh, Y., and Ohsaki, H. (2015). Recognizing depression from Twitter activity. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems - CHI 15.
WHO (2015). Comprehensive mental health action plan 2013–2020.
WHO (2017). Depression.
Aldarwish, M. M. and Ahmad, H. F. (2017). Predicting depression levels using social media posts. 2017 IEEE 13th International Symposium on Autonomous Decentralized System (ISADS).
Balage Filho, P. P., Pardo, T. A., and Aluı́sio, S. M. (2013). An evaluation of the BrazilianPortuguese LIWC dictionary for sentiment analysis. In Proceedings of the 9th Brazilian Symposium in Information and Human Language Technology (STIL), pages 215–219.
Bradley, M., L. P. and Cuthbert, B. (1999). Affective norms for english words (ANEW). Gainsville, Fla, NIMH Centre for the Study of Emotion and Attention, University of Florida.
Bradley, M. M. and Lang, P. J. (1999). Affective norms for English words (ANEW): Instruction manual and affective ratings.
Chen, X., Sykora, M. D., Jackson, T. W., and Elayan, S. (2018). What about mood swings: Identifying depression on Twitter with temporal measures of emotions. In Companion Proceedings of the The Web Conference 2018, WWW ’18, pages 1653– 1660, Republic and Canton of Geneva, Switzerland. International World Wide Web Conferences Steering Committee.
DSM-IV (2011). Diagnostic and statistical manual of mental disorders: DSM-IV-TR, volume IV. American Psychiatric Association.
DSM-V (2013). Diagnostic and statistical manual of mental disorders, volume V. Ame- rican Psychiatric Publishing.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The weka data mining software: an update. ACM SIGKDD explorations newsletter, 11(1):10–18.
IBGE (2013). Pesquisa nacional de saúde: 2013 : percepção do estado de saúde, estilos de vida e doenças crônicas: Brasil, grandes regiões e unidades da federação.
IBGE (2017). Pesquisa nacional por amostra de domicı́lios contı́nua - pnad contı́nua.
Kristensen, C. H., Gomes, C. F. A. d. A., Justo, A. R., and Vieira, K. (2011). Normas brasileiras para o Affective Norms for English Words. Trends in Psychiatry and Psy- chotherapy, 33:135 – 146.
Mate, S. and Curcio, I. (2009). Mobile and interactive social television. IEEE Communi- cations Magazine, 47(12):116–122.
Nascimento, R., Cruz, P., dos Santos, G., and Guedes, G. P. (2018). Identificando sinais de comportamento depressivo em redes sociais. In 7th Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2018), Natal, RN, Brasil. SBC.
Nguyen, T., Phung, D., Dao, B., Venkatesh, S., and Berk, M. (2014). Affective and content analysis of online depression communities. IEEE Transactions on Affective Computing, 5(3):217–226.
Pennebaker, J. W. (2011). The secret life of pronouns: what our words say about us. Bloomsbury Press.
PNAD, I. (2017). Acesso à internet e à televisão e posse de telefone móvel celular para uso pessoal : 2017.
Radloff, L. S. (1977). The CES-D scale: a self-report depression scale for research in the general population. Applied psychological measurement, 1(3):385–401.
Rude, S., Gortner, E.-M., and Pennebaker, J. (2004). Language use of depressed and depression-vulnerable college students. Cognition & Emotion, 18(8):1121–1133.
Silveira, B., da Silva, A. P. C., and Murai, F. (2018). Análise de comunidades de suporte a transtornos de saúde mental do Reddit. In 7th Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2018), Natal, RN, Brasil. SBC.
Tausczik, Y. R. and Pennebaker, J. W. (2009). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psy- chology, 29(1):24–54.
Tsugawa, S., Kikuchi, Y., Kishino, F., Nakajima, K., Itoh, Y., and Ohsaki, H. (2015). Recognizing depression from Twitter activity. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems - CHI 15.
WHO (2015). Comprehensive mental health action plan 2013–2020.
WHO (2017). Depression.
Publicado
09/07/2019
Como Citar
NASCIMENTO, Rodolpho da Silva; CARVALHO, Flavio ; GUEDES, Gustavo Paiva .
Identificando sintomas depressivos: um estudo de caso no Youtube. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 8. , 2019, Belém.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 119-130.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2019.6554.
