Computational methods for the identification of suicidal ideation: a systematic review

TL;DR

This paper reviews computational methods for identifying suicidal ideation in text, focusing on NLP techniques like BERT and hybrid models.

Contribution

A systematic review of computational methods for detecting suicidal ideation using natural language processing.

Findings

Transformer-based models like BERT and hybrid methods are commonly used for detecting suicidal ideation.

Most studies use data from social networks, which limits linguistic and cultural diversity.

Feature extraction techniques like TF-IDF and pre-trained embeddings improve model performance.

Abstract

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.

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.

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 neural networks such as CNN and LSTM. There are also approaches with hierarchical attention mechanisms. Some studies employed additional techniques such as feature extraction with TF-IDF and pre-trained embeddings to improve model performance.

Limitations in the evidence include the lack of linguistic and cultural diversity and the predominance of data from social networks. These results indicate that computational techniques have high potential to support early prevention strategies for suicidal ideation. However, expanding the diversity of linguistic contexts and improving understanding of the models among non-experts, such as physicians and other interested individuals, is necessary.

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