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

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Λεπτομέρειες βιβλιογραφικής εγγραφής
Εκδόθηκε σε:	Programming and Computer Software vol. 50, no. 8 (Dec 2024), p. 612
Έκδοση:	Springer Nature B.V.
Θέματα:	Suicidal ideation Datasets Large language models Social networks Machine learning Artificial neural networks Digital media Support vector machines Suicides & suicide attempts
Διαθέσιμο Online:	Citation/Abstract Full Text Full Text - PDF
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Περιγραφή
Περίληψη:	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
Πηγή:	Advanced Technologies & Aerospace Database