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 |
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| Publicado: |
Springer Nature B.V.
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 0361-7688 | ||
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| 024 | 7 | |a 10.1134/S0361768824700658 |2 doi | |
| 035 | |a 3154524485 | ||
| 045 | 2 | |b d20241201 |b d20241231 | |
| 245 | 1 | |a Word Embeddings and Machine Learning Classifiers Applications for Automatic Detection of Suicide Tendencies in Social Media | |
| 260 | |b Springer Nature B.V. |c Dec 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Suicidal ideation | ||
| 653 | |a Datasets | ||
| 653 | |a Large language models | ||
| 653 | |a Social networks | ||
| 653 | |a Machine learning | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Digital media | ||
| 653 | |a Support vector machines | ||
| 653 | |a Suicides & suicide attempts | ||
| 773 | 0 | |t Programming and Computer Software |g vol. 50, no. 8 (Dec 2024), p. 612 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3154524485/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3154524485/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3154524485/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |