Detecting Suicidal Ideation on Tweets - Cardoso, Vinícius; Silva, Antonio Fhillipi; Sinoara, Roberta; Rezende, Solange; Calçada, Dario (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2019: Anais do XVI Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2019.9282, 2019)
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.