Wallace Da Silva Coelho Valadão
ABSTRACT
Nowadays, suicide is one of the leading causes of death for young people worldwide. Many of those youngsters expose their suicidal intentions on social media. Prevention based on suicide ideation (SI) detection in social media posts is an important strategy to avoid the occurrence of this type of death. Although several studies have developed methods to automatically detect SI in texts, as far as it was possible to observe, none of them uses contextualized embeddings (i.e. vector representations of texts that consider the context where words and sentences occur). Therefore, the present work hypothesizes that representing texts with contextualized embeddings (CE) can improve SI detection. Hence, this article proposes a method that combines CE with classification models generated by machine learning algorithms, to detect SI. The results obtained in the preliminary experiments with the proposed method presented pieces of evidence that the raised hypothesis is valid.
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Index Terms
- A Contextualized Embeddings-based Method to Detect Suicide Ideations in Texts
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