Identificando sintomas de depressão em postagens do Twitter em português do Brasil
Resumo
A depressão é uma das questões de saúde mental mais preocupantes da atualidade. No Brasil, em 2019, 10,2% da população adulta relatou ter sido diagnosticada com depressão segundo dados da Pesquisa Nacional de Saúde. Identificar pessoas com perfil possivelmente depressivo permite um acompanhamento adequado por parte dos profissionais de saúde mental. Nesse sentido, as redes sociais online, como o Twitter, podem ser importantes aliadas. Este artigo apresenta experimentos realizados para a classificação automática de postagens (e não usuários) do Twitter contendo conteúdo que denota algum sintoma de depressão. A classificação com regressão logística apresentou os melhores resultados (F 1 média de 57%) entre os algoritmos investigados.Referências
American Psychiatric Association et al. (2014). DSM-5: Manual diagnóstico e estatístico de transtornos mentais. Artmed Editora.
BECK, A. T., WARD, C. H., MENDELSON, M., MOCK, J., e ERBAUGH, J. (1961). An Inventory for Measuring Depression. Archives of General Psychiatry, 4(6):561–571.
Bertaglia, T. F. C. e Nunes, M. d. G. V. (2016). Exploring word embeddings for unIn Proceedings of the 2nd supervised textual user-generated content normalization. Workshop on Noisy User-generated Text (WNUT), pages 112–120.
Brum, H. e Nunes, M. d. G. V. (2018). Building a Sentiment Corpus of Tweets in Brazilian Portuguese. In chair), N. C. C., Choukri, K., Cieri, C., Declerck, T., Goggi, S., Hasida, K., Isahara, H., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., Piperidis, S., e Tokunaga, T., editors, Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).
De Choudhury, M., Counts, S., e Horvitz, E. (2013). Social media as a measurement tool of depression in populations. In Proceedings of the 5th Annual ACM Web Science Conference, WebSci ’13, page 47–56, New York, NY, USA. Association for Computing Machinery.
Duque, J. W. G., Raymundo, A. L., e Neto, P. F. (2018). Uma aplicação de big data para classificação de sentenças depressivas do twitter. Revista H-TEC Humanidades e Tecnologia, 2(1):82–95.
Eichstaedt, J. C., Smith, R. J., Merchant, R. M., Ungar, L. H., Crutchley, P., Preot¸iucPietro, D., Asch, D. A., e Schwartz, H. A. (2018). Facebook language predicts depression in medical records. volume 115, pages 11203–11208. National Academy of Sciences.
Ji, S., Yu, C., Fung, S.-f., Pan, S., e Long, G. (2018). Supervised learning for suicidal ideation detection in online user content. Complexity, 2018:1–10.
Mowery, D. L., Park, A., Bryan, C., e Conway, M. (2016). Towards automatically classifying depressive symptoms from Twitter data for population health. In Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES), pages 182–191, Osaka, Japan. The COLING 2016 Organizing Committee.
O’Dea, B., Wan, S., Batterham, P. J., Calear, A. L., Paris, C., e Christensen, H. (2015). Detecting suicidality on twitter. Internet Interventions, 2(2):183–188.
Pestian, J., Matykiewicz, P., Linn-Gust, M., South, B., Uzuner, O., Wiebe, J., Cohen, K., Hurdle, J., e Brew, C. (2012). Sentiment analysis of suicide notes: A shared task. Biomedical informatics insights, 5:3–16.
Santos, W., Funabashi, A., e Paraboni, I. (2020). Searching brazilian twitter for signs of mental health issues. In Proceedings of The 12th Language Resources and Evaluation Conference, pages 6111–6117, Marseille, France. European Language Resources Association.
Souza, F., Nogueira, R., e Lotufo, R. (2020). BERTimbau: pretrained BERT models for Brazilian Portuguese. In 9th Brazilian Conference on Intelligent Systems, BRACIS, Rio Grande do Sul, Brazil, October 20-23 (to appear).
Spitzer, R. L., Kroenke, K., Williams, J. B., Group, P. H. Q. P. C. S., Group, P. H. Q. P. C. S., et al. (1999). Validation and utility of a self-report version of prime-md: the phq primary care study. Jama, 282(18):1737–1744.
Yadav, S., Chauhan, J., Sain, J. P., Thirunarayan, K., Sheth, A., e Schumm, J. (2020). Identifying depressive symptoms from tweets: Figurative language enabled multitask learning framework. In Proceedings of the 28th International Conference on Computational Linguistics, pages 696–709, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Yazdavar, A. H., Al-Olimat, H. S., Ebrahimi, M., Bajaj, G., Banerjee, T., Thirunarayan, K., Pathak, J., e 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, pages 1191–1198.
Ziwei, B. Y. e Chua, H. N. (2019). An application for classifying depression in tweets. In Proceedings of the 2nd International Conference on Computing and Big Data, ICCBD 2019, page 37–41, New York, NY, USA. Association for Computing Machinery.
BECK, A. T., WARD, C. H., MENDELSON, M., MOCK, J., e ERBAUGH, J. (1961). An Inventory for Measuring Depression. Archives of General Psychiatry, 4(6):561–571.
Bertaglia, T. F. C. e Nunes, M. d. G. V. (2016). Exploring word embeddings for unIn Proceedings of the 2nd supervised textual user-generated content normalization. Workshop on Noisy User-generated Text (WNUT), pages 112–120.
Brum, H. e Nunes, M. d. G. V. (2018). Building a Sentiment Corpus of Tweets in Brazilian Portuguese. In chair), N. C. C., Choukri, K., Cieri, C., Declerck, T., Goggi, S., Hasida, K., Isahara, H., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., Piperidis, S., e Tokunaga, T., editors, Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).
De Choudhury, M., Counts, S., e Horvitz, E. (2013). Social media as a measurement tool of depression in populations. In Proceedings of the 5th Annual ACM Web Science Conference, WebSci ’13, page 47–56, New York, NY, USA. Association for Computing Machinery.
Duque, J. W. G., Raymundo, A. L., e Neto, P. F. (2018). Uma aplicação de big data para classificação de sentenças depressivas do twitter. Revista H-TEC Humanidades e Tecnologia, 2(1):82–95.
Eichstaedt, J. C., Smith, R. J., Merchant, R. M., Ungar, L. H., Crutchley, P., Preot¸iucPietro, D., Asch, D. A., e Schwartz, H. A. (2018). Facebook language predicts depression in medical records. volume 115, pages 11203–11208. National Academy of Sciences.
Ji, S., Yu, C., Fung, S.-f., Pan, S., e Long, G. (2018). Supervised learning for suicidal ideation detection in online user content. Complexity, 2018:1–10.
Mowery, D. L., Park, A., Bryan, C., e Conway, M. (2016). Towards automatically classifying depressive symptoms from Twitter data for population health. In Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES), pages 182–191, Osaka, Japan. The COLING 2016 Organizing Committee.
O’Dea, B., Wan, S., Batterham, P. J., Calear, A. L., Paris, C., e Christensen, H. (2015). Detecting suicidality on twitter. Internet Interventions, 2(2):183–188.
Pestian, J., Matykiewicz, P., Linn-Gust, M., South, B., Uzuner, O., Wiebe, J., Cohen, K., Hurdle, J., e Brew, C. (2012). Sentiment analysis of suicide notes: A shared task. Biomedical informatics insights, 5:3–16.
Santos, W., Funabashi, A., e Paraboni, I. (2020). Searching brazilian twitter for signs of mental health issues. In Proceedings of The 12th Language Resources and Evaluation Conference, pages 6111–6117, Marseille, France. European Language Resources Association.
Souza, F., Nogueira, R., e Lotufo, R. (2020). BERTimbau: pretrained BERT models for Brazilian Portuguese. In 9th Brazilian Conference on Intelligent Systems, BRACIS, Rio Grande do Sul, Brazil, October 20-23 (to appear).
Spitzer, R. L., Kroenke, K., Williams, J. B., Group, P. H. Q. P. C. S., Group, P. H. Q. P. C. S., et al. (1999). Validation and utility of a self-report version of prime-md: the phq primary care study. Jama, 282(18):1737–1744.
Yadav, S., Chauhan, J., Sain, J. P., Thirunarayan, K., Sheth, A., e Schumm, J. (2020). Identifying depressive symptoms from tweets: Figurative language enabled multitask learning framework. In Proceedings of the 28th International Conference on Computational Linguistics, pages 696–709, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Yazdavar, A. H., Al-Olimat, H. S., Ebrahimi, M., Bajaj, G., Banerjee, T., Thirunarayan, K., Pathak, J., e 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, pages 1191–1198.
Ziwei, B. Y. e Chua, H. N. (2019). An application for classifying depression in tweets. In Proceedings of the 2nd International Conference on Computing and Big Data, ICCBD 2019, page 37–41, New York, NY, USA. Association for Computing Machinery.
Publicado
29/11/2021
Como Citar
MENDES, Augusto R.; PASSADOR, Rafael V. P.; CASELI, Helena M..
Identificando sintomas de depressão em postagens do Twitter em português do Brasil. In: SIMPÓSIO BRASILEIRO DE TECNOLOGIA DA INFORMAÇÃO E DA LINGUAGEM HUMANA (STIL), 13. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 162-171.
DOI: https://doi.org/10.5753/stil.2021.17795.
