Computational methods for the identification of suicidal ideation: a systematic review
Abstract
Introduction: Suicide is one of the leading causes of death among young people, to the extent that in many countries it is considered a public health issue. It is important to attempt to reduce the growth of this trend, especially among susceptible individuals, considering that it increased because of the COVID-19 pandemic. Natural language processing (NLP) provides various tools that allow for the analysis of texts to predict the presence of suicidal ideation. This work aims to conduct a systematic literature review to extract the computational techniques for identifying suicidal ideation in texts written in natural language.
Methods: The PRISMA 2020 method was used, which was divided into nine phases, and three inclusion criteria and two exclusion criteria were established for the selection of studies. The searches were conducted through high-level academic databases such as Scopus, IEEE Xplore, ACM Digital Library, Springer, and Web of Science. The risk of bias was assessed using AMSTAR 2. Potential biases identified include a lack of linguistic and cultural diversity and the predominance of data from social networks. A narrative synthesis was used to analyze and compare the findings qualitatively.
Results: In the end, 25 studies related to computational methods for detecting suicidal ideation in texts written in natural language were identified. The techniques mainly focus on transformer-based models such as BERT and hybrid methods, which combine this architecture with…
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