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Deep neural networks detect suicide risk from textual facebook posts
Detection of suicide risk is a highly prioritized, yet complicated task. Five decades of research have produced predictions slightly better than chance (AUCs = 0.56–0.58). In this study, Artificial Neural Network (ANN) models were constructed to predict suicide risk from everyday language of social...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7542168/ https://www.ncbi.nlm.nih.gov/pubmed/33028921 http://dx.doi.org/10.1038/s41598-020-73917-0 |
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author | Ophir, Yaakov Tikochinski, Refael Asterhan, Christa S. C. Sisso, Itay Reichart, Roi |
author_facet | Ophir, Yaakov Tikochinski, Refael Asterhan, Christa S. C. Sisso, Itay Reichart, Roi |
author_sort | Ophir, Yaakov |
collection | PubMed |
description | Detection of suicide risk is a highly prioritized, yet complicated task. Five decades of research have produced predictions slightly better than chance (AUCs = 0.56–0.58). In this study, Artificial Neural Network (ANN) models were constructed to predict suicide risk from everyday language of social media users. The dataset included 83,292 postings authored by 1002 authenticated Facebook users, alongside valid psychosocial information about the users. Using Deep Contextualized Word Embeddings for text representation, two models were constructed: A Single Task Model (STM), to predict suicide risk from Facebook postings directly (Facebook texts → suicide) and a Multi-Task Model (MTM), which included hierarchical, multilayered sets of theory-driven risk factors (Facebook texts → personality traits → psychosocial risks → psychiatric disorders → suicide). Compared with the STM predictions (0.621 ≤ AUC ≤ 0.629), the MTM produced significantly improved prediction accuracy (0.697 ≤ AUC ≤ 0.746), with substantially larger effect sizes (0.729 ≤ d ≤ 0.936). Subsequent content analyses suggested that predictions did not rely on explicit suicide-related themes, but on a range of text features. The findings suggest that machine learning based analyses of everyday social media activity can improve suicide risk predictions and contribute to the development of practical detection tools. |
format | Online Article Text |
id | pubmed-7542168 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75421682020-10-08 Deep neural networks detect suicide risk from textual facebook posts Ophir, Yaakov Tikochinski, Refael Asterhan, Christa S. C. Sisso, Itay Reichart, Roi Sci Rep Article Detection of suicide risk is a highly prioritized, yet complicated task. Five decades of research have produced predictions slightly better than chance (AUCs = 0.56–0.58). In this study, Artificial Neural Network (ANN) models were constructed to predict suicide risk from everyday language of social media users. The dataset included 83,292 postings authored by 1002 authenticated Facebook users, alongside valid psychosocial information about the users. Using Deep Contextualized Word Embeddings for text representation, two models were constructed: A Single Task Model (STM), to predict suicide risk from Facebook postings directly (Facebook texts → suicide) and a Multi-Task Model (MTM), which included hierarchical, multilayered sets of theory-driven risk factors (Facebook texts → personality traits → psychosocial risks → psychiatric disorders → suicide). Compared with the STM predictions (0.621 ≤ AUC ≤ 0.629), the MTM produced significantly improved prediction accuracy (0.697 ≤ AUC ≤ 0.746), with substantially larger effect sizes (0.729 ≤ d ≤ 0.936). Subsequent content analyses suggested that predictions did not rely on explicit suicide-related themes, but on a range of text features. The findings suggest that machine learning based analyses of everyday social media activity can improve suicide risk predictions and contribute to the development of practical detection tools. Nature Publishing Group UK 2020-10-07 /pmc/articles/PMC7542168/ /pubmed/33028921 http://dx.doi.org/10.1038/s41598-020-73917-0 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Ophir, Yaakov Tikochinski, Refael Asterhan, Christa S. C. Sisso, Itay Reichart, Roi Deep neural networks detect suicide risk from textual facebook posts |
title | Deep neural networks detect suicide risk from textual facebook posts |
title_full | Deep neural networks detect suicide risk from textual facebook posts |
title_fullStr | Deep neural networks detect suicide risk from textual facebook posts |
title_full_unstemmed | Deep neural networks detect suicide risk from textual facebook posts |
title_short | Deep neural networks detect suicide risk from textual facebook posts |
title_sort | deep neural networks detect suicide risk from textual facebook posts |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7542168/ https://www.ncbi.nlm.nih.gov/pubmed/33028921 http://dx.doi.org/10.1038/s41598-020-73917-0 |
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