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Word Embeddings and Machine Learning Classifiers Applications for Automatic Detection of Suicide Tendencies in Social Media

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Publicado en:Programming and Computer Software vol. 50, no. 8 (Dec 2024), p. 612
Publicado:
Springer Nature B.V.
Materias:
Suicidal ideation
Datasets
Large language models
Social networks
Machine learning
Artificial neural networks
Digital media
Support vector machines
Suicides & suicide attempts
Acceso en línea:Citation/Abstract
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Resumen:This study presents an innovative and comprehensive model for the automatic detection of suicidal ideation in social media posts. Through an in-depth analysis of 50000 posts and the combination of four word embedding techniques (Word2Vec, GloVe, MPNet, and GPT-3) with five advanced classifiers, we have achieved an accuracy of over 90% in identifying users who may be at risk. Our results suggest that the integration of large language models like GPT-3 with deep neural network architectures offers a promising tool for suicide prevention in the digital realm, contributing to the development of automated screening systems capable of alerting mental health professionals to potential cases of risk.
ISSN:0361-7688
1608-3261
DOI:10.1134/S0361768824700658
Fuente:Advanced Technologies & Aerospace Database