Machine Learning for Suicidal Ideation Identification on Twitter for the Portuguese Language

Abstract

Suicidal ideation is one of the main predictors of the risk of suicide attempt and can be described as thoughts, ideas, planning, and desire to commit suicide. Fast detection of such ideation in early stages is essential for effective treatment. Many expressions of suicidal ideation can be found in publications in social networks, especially by young people. Previous works explore the automatic detection of suicidal ideation in social networks for the English language using machine learning algorithms. In this work, we present the first exploration of machine learning algorithms for suicidal ideation detection for the Portuguese language. We compared three classifiers in Twitter data: SVM, LSTM, and BERT (multilingual and Portuguese). Results suggest that BERT is effective for suicidal ideation identification in Portuguese data, achieving 79% of F1 score and less than 9% false negative score.