Detecting Suicidal Ideation on Tweets

  • Vinícius Cardoso Universidade Estadual do Piauí
  • Antonio Fhillipi Silva Universidade Estadual do Piauí
  • Roberta Sinoara Universidade de São Paulo / Instituto Federal de Educação, Ciência e Tecnologia de São Paulo
  • Solange Rezende Universidade de São Paulo
  • Dario Calçada Universidade Estadual do Piauí / Universidade de São Paulo


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


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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: