Detecting Suicidal Ideation on Tweets
Resumo
According to the World Health Organization, every 40 seconds, one person dies of suicide in the world. Among young people aged from 15 to 29, suicide is the second leading cause of death. Still, these deaths can be prevented. In this scenario, social networks like Twitter can become real-time sources of information and help to prevent suicide. This paper presents an initial exploration of the problem of identifying individuals at risk of self-extermination in social networks that use Portuguese language. As a main scientific contribution, a set of tweet data, manually labeled by experts, was built and can be used for future research on the subject. As a preliminary evaluation, we applied machine learning algorithms for classification. The results indicate that the dataset can be used in a study to develop a real-time suicidal ideation tweet detection system.
Palavras-chave:
Dataset, Natural Language Processing, Suicidal Ideation, Tweets
Referências
Abboute, A., Boudjeriou, Y., Entringer, G., Azé, J., Bringay, S., and Poncelet, P. (2014). Mining twitter for suicide prevention. In International Conference on Applications of Natural Language to Data Bases/Information Systems, pages 250–253. Springer.
AFSP (2013). Risk factors and warning signs. American Foundation for Suicide Prevention.
Aggarwal, C. C. and Zhai, C. (2012). Mining Text Data. Springer, 1 edition.
Bailey, R. K., Patel, T. C., Avenido, J., Patel, M., Jaleel, M., Barker, N. C., Khan, J. A., All, S., and Jabeen, S. (2011). Suicide: current trends. Journal of the National Medical Association, 103(7):614–617.
Burnap, P., Colombo, G., Amery, R., Hodorog, A., and Scourfield, J. (2017). Multiclass machine classification of suicide-related communication on twitter. Online social networks and media, 2:32–44.
De Choudhury, M., Gamon, M., Counts, S., and Horvitz, E. (2013). Predicting depression via social media. In Seventh international AAAI conference on weblogs and social media.
Desmet, B. and Hoste, V. (2014). Recognising suicidal messages in dutch social media. In 9th international conference on language resources and evaluation (LREC), pages 830–835.
Go, A., Huang, L., and Bhayani, R. (2009). Twitter sentiment analysis. Entropy, 17:252.
Gunn, J. F. and Lester, D. (2015). Twitter postings and suicide: An analysis of the postings of a fatal suicide in the 24 hours prior to death. Suicidologi, 17(3).
Jashinsky, J., Burton, S. H., Hanson, C. L., West, J., Giraud-Carrier, C., Barnes, M. D., and Argyle, T. (2014). Tracking suicide risk factors through twitter in the us. Crisis.
Kailasam, V. and Samuels, E. (2015). Can social media help mental health practitioners prevent suicides? Current Psychiatry, 14(2):37–51.
McCarthy, M. J. (2010). Internet monitoring of suicide risk in the population. Journal of affective disorders, 122(3):277–279.
Miner, G., Elder, J., Hill, T., Nisbet, R., Delen, D., and Fast, A. (2012). Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications. Academic Press, 1 edition.
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.
O’Dea, B., Wan, S., Batterham, P. J., Calear, A. L., Paris, C., and Christensen, H. (2015). Detecting suicidality on twitter. Internet Interventions, 2(2):183–188.
OMS (2014). Preventing suicide: A global imperative. World Health Organization.
Rossi, R. G. (2016). Classificação automática de textos por meio de aprendizado de máquina baseado em redes. PhD thesis, Universidade de São Paulo.
Rossi, R. G., Lopes, A. A., and Rezende, S. O. (2016). Optimization and label propagation in bipartite heterogeneous networks to improve transductive classification of texts. Information Processing and Management, 52(2):217–257.
Rossi, R. G., Lopes, A. d. A., Faleiros, T. d. P., and Rezende, S. O. (2014). Inductive model generation for text classification using a bipartite heterogeneous network. Journal of Computer Science and Technology, 29(3):361–375.
Sinoara, R. A., Antunes, J., and Rezende, S. O. (2017). Text mining and semantics: a systematic mapping study. Journal of the Brazilian Computer Society, 23(9):1–20.
Sueki, H. (2015). The association of suicide-related twitter use with suicidal behaviour: a cross-sectional study of young internet users in japan. Journal of affective disorders, 170:155–160.
Witten, I. H. and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques. Elsevier, 2 edition.
AFSP (2013). Risk factors and warning signs. American Foundation for Suicide Prevention.
Aggarwal, C. C. and Zhai, C. (2012). Mining Text Data. Springer, 1 edition.
Bailey, R. K., Patel, T. C., Avenido, J., Patel, M., Jaleel, M., Barker, N. C., Khan, J. A., All, S., and Jabeen, S. (2011). Suicide: current trends. Journal of the National Medical Association, 103(7):614–617.
Burnap, P., Colombo, G., Amery, R., Hodorog, A., and Scourfield, J. (2017). Multiclass machine classification of suicide-related communication on twitter. Online social networks and media, 2:32–44.
De Choudhury, M., Gamon, M., Counts, S., and Horvitz, E. (2013). Predicting depression via social media. In Seventh international AAAI conference on weblogs and social media.
Desmet, B. and Hoste, V. (2014). Recognising suicidal messages in dutch social media. In 9th international conference on language resources and evaluation (LREC), pages 830–835.
Go, A., Huang, L., and Bhayani, R. (2009). Twitter sentiment analysis. Entropy, 17:252.
Gunn, J. F. and Lester, D. (2015). Twitter postings and suicide: An analysis of the postings of a fatal suicide in the 24 hours prior to death. Suicidologi, 17(3).
Jashinsky, J., Burton, S. H., Hanson, C. L., West, J., Giraud-Carrier, C., Barnes, M. D., and Argyle, T. (2014). Tracking suicide risk factors through twitter in the us. Crisis.
Kailasam, V. and Samuels, E. (2015). Can social media help mental health practitioners prevent suicides? Current Psychiatry, 14(2):37–51.
McCarthy, M. J. (2010). Internet monitoring of suicide risk in the population. Journal of affective disorders, 122(3):277–279.
Miner, G., Elder, J., Hill, T., Nisbet, R., Delen, D., and Fast, A. (2012). Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications. Academic Press, 1 edition.
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.
O’Dea, B., Wan, S., Batterham, P. J., Calear, A. L., Paris, C., and Christensen, H. (2015). Detecting suicidality on twitter. Internet Interventions, 2(2):183–188.
OMS (2014). Preventing suicide: A global imperative. World Health Organization.
Rossi, R. G. (2016). Classificação automática de textos por meio de aprendizado de máquina baseado em redes. PhD thesis, Universidade de São Paulo.
Rossi, R. G., Lopes, A. A., and Rezende, S. O. (2016). Optimization and label propagation in bipartite heterogeneous networks to improve transductive classification of texts. Information Processing and Management, 52(2):217–257.
Rossi, R. G., Lopes, A. d. A., Faleiros, T. d. P., and Rezende, S. O. (2014). Inductive model generation for text classification using a bipartite heterogeneous network. Journal of Computer Science and Technology, 29(3):361–375.
Sinoara, R. A., Antunes, J., and Rezende, S. O. (2017). Text mining and semantics: a systematic mapping study. Journal of the Brazilian Computer Society, 23(9):1–20.
Sueki, H. (2015). The association of suicide-related twitter use with suicidal behaviour: a cross-sectional study of young internet users in japan. Journal of affective disorders, 170:155–160.
Witten, I. H. and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques. Elsevier, 2 edition.
Publicado
15/10/2019
Como Citar
CARDOSO, Vinícius; SILVA, Antonio Fhillipi; SINOARA, Roberta; REZENDE, Solange; CALÇADA, Dario.
Detecting Suicidal Ideation on Tweets. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 178-189.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9282.
