A Contextualized Embeddings-based Method to Detect Suicide Ideations in Texts
Authors: 
Wallace Da Silva Coelho Valadão, Eric Rodrigues Da Silva, Paulo Márcio Souza Freire, Ronaldo Ribeiro GoldschmidtAuthors Info & Claims
Pages 41 - 45
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
Index terms have been assigned to the content through auto-classification.
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Published In
October 2023
285 pages
ISBN:9798400709081
DOI:10.1145/3617023
Copyright © 2023 ACM.
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Published: 23 October 2023
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WebMedia '23
WebMedia '23: Brazilian Symposium on Multimedia and the Web
October 23 - 27, 2023
Ribeirão Preto, Brazil
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