Identificando Sinais de Comportamento Depressivo em Redes Sociais
Resumo
Existem mais de 300 milhões de indivíduos que sofrem de depressão no mundo. Dentre as diversas consequências provenientes desse transtorno, a mais catastrófica é o suicídio. Nesse aspecto, diversos indivíduos manifestam em redes sociais os sinais depressivos antes de chegar ao extremo do suicídio. Nesse cenário, o presente estudo tem objetiva construir um conjunto de dados de palavras relacionadas à depressão para a língua portuguesa. Experimentos preliminares indicam que o conjunto de dados criado auxilia na classificação de emoções em textos de uma rede social brasileira.
Referências
Balage Filho, P. P., Pardo, T. A. S., and Aluísio, S. M. (2013). An evaluation of the brazilian portuguese liwc dictionary for sentiment analysis. In Proceedings of the 9th Brazilian Symposium in Information and Human Language Technology.
De Choudhury, M., Gamon, M., Counts, S., and Horvitz, E. (2013). Predicting depression via social media. ICWSM, 13:1–10.
Ekman, P. (1992). An argument for basic emotions. Cognition & emotion, 6(3-4):169–200.
Goh, T.-T. and Huang, Y.-P. (2009). Monitoring youth depression risk in web 2.0. VINE, 39(3):192–202.
Haag Kristensen, C., Azevedo Gomes, C. F., Reuwsaat Justo, A., and Vieira, K. (2011). Normas brasileiras para o affective norms for english words. Trends in Psychiatry and Psychotherapy, 33(3).
Moreno, M. A., Jelenchick, L. A., Egan, K. G., Cox, E., Young, H., Gannon, K. E., and Becker, T. (2011). Feeling bad on facebook: Depression disclosures by college students on a social networking site. Depression and anxiety, 28(6):447–455.
Nambisan, P., Luo, Z., Kapoor, A., Patrick, T. B., and Cisler, R. A. (2015). Social media, big data, and public health informatics: Ruminating behavior of depression revealed through twitter. 2015 48th Hawaii International Conference on System Sciences.
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.
Organization, W. H. (2001). The World Health Report 2001: Mental health: new understanding, new hope. World Health Organization.
Rude, S., Gortner, E.-M., and Pennebaker, J. (2004). Language use of depressed and depression-vulnerable college students. Cognition & Emotion, 18(8):1121–1133.
SECOM (2015). Pesquisa brasileira de mídia 2015. url: [link].
Shen, Y.-C., Kuo, T.-T., Yeh, I.-N., Chen, T.-T., and Lin, S.-D. (2013). Exploiting temporal information in a two-stage classification framework for content-based depression detection. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 276–288. Springer.
WHO (2015). Comprehensive mental health action plan 2013–2020. url: [link].
WHO (2017). "depression: let’s talk"says who, as depression tops list of causes of ill health. url: [link].
WHO (2018). Depression. url: [link].
De Choudhury, M., Gamon, M., Counts, S., and Horvitz, E. (2013). Predicting depression via social media. ICWSM, 13:1–10.
Ekman, P. (1992). An argument for basic emotions. Cognition & emotion, 6(3-4):169–200.
Goh, T.-T. and Huang, Y.-P. (2009). Monitoring youth depression risk in web 2.0. VINE, 39(3):192–202.
Haag Kristensen, C., Azevedo Gomes, C. F., Reuwsaat Justo, A., and Vieira, K. (2011). Normas brasileiras para o affective norms for english words. Trends in Psychiatry and Psychotherapy, 33(3).
Moreno, M. A., Jelenchick, L. A., Egan, K. G., Cox, E., Young, H., Gannon, K. E., and Becker, T. (2011). Feeling bad on facebook: Depression disclosures by college students on a social networking site. Depression and anxiety, 28(6):447–455.
Nambisan, P., Luo, Z., Kapoor, A., Patrick, T. B., and Cisler, R. A. (2015). Social media, big data, and public health informatics: Ruminating behavior of depression revealed through twitter. 2015 48th Hawaii International Conference on System Sciences.
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.
Organization, W. H. (2001). The World Health Report 2001: Mental health: new understanding, new hope. World Health Organization.
Rude, S., Gortner, E.-M., and Pennebaker, J. (2004). Language use of depressed and depression-vulnerable college students. Cognition & Emotion, 18(8):1121–1133.
SECOM (2015). Pesquisa brasileira de mídia 2015. url: [link].
Shen, Y.-C., Kuo, T.-T., Yeh, I.-N., Chen, T.-T., and Lin, S.-D. (2013). Exploiting temporal information in a two-stage classification framework for content-based depression detection. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 276–288. Springer.
WHO (2015). Comprehensive mental health action plan 2013–2020. url: [link].
WHO (2017). "depression: let’s talk"says who, as depression tops list of causes of ill health. url: [link].
WHO (2018). Depression. url: [link].
Publicado
26/07/2018
Como Citar
NASCIMENTO, Rodolpho da Silva; PARREIRA, Pedro; DOS SANTOS, Gabriel Nascimento; GUEDES, Gustavo Paiva.
Identificando Sinais de Comportamento Depressivo em Redes Sociais. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 7. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 235-240.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2018.3597.
