Deep neural networks detect suicide risk from textual facebook posts

Abstract 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 o...

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Autores principales:	Yaakov Ophir, Refael Tikochinski, Christa S. C. Asterhan, Itay Sisso, Roi Reichart
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f2eff099f81a47a5be67597ff0d20008
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spelling	oai:doaj.org-article:f2eff099f81a47a5be67597ff0d200082021-12-02T18:36:14ZDeep neural networks detect suicide risk from textual facebook posts10.1038/s41598-020-73917-02045-2322https://doaj.org/article/f2eff099f81a47a5be67597ff0d200082020-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-73917-0https://doaj.org/toc/2045-2322Abstract 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.Yaakov OphirRefael TikochinskiChrista S. C. AsterhanItay SissoRoi ReichartNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yaakov Ophir Refael Tikochinski Christa S. C. Asterhan Itay Sisso Roi Reichart Deep neural networks detect suicide risk from textual facebook posts
description	Abstract 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	article
author	Yaakov Ophir Refael Tikochinski Christa S. C. Asterhan Itay Sisso Roi Reichart
author_facet	Yaakov Ophir Refael Tikochinski Christa S. C. Asterhan Itay Sisso Roi Reichart
author_sort	Yaakov Ophir
title	Deep neural networks detect suicide risk from textual facebook posts
title_short	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_sort	deep neural networks detect suicide risk from textual facebook posts
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/f2eff099f81a47a5be67597ff0d20008
work_keys_str_mv	AT yaakovophir deepneuralnetworksdetectsuicideriskfromtextualfacebookposts AT refaeltikochinski deepneuralnetworksdetectsuicideriskfromtextualfacebookposts AT christascasterhan deepneuralnetworksdetectsuicideriskfromtextualfacebookposts AT itaysisso deepneuralnetworksdetectsuicideriskfromtextualfacebookposts AT roireichart deepneuralnetworksdetectsuicideriskfromtextualfacebookposts
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Deep neural networks detect suicide risk from textual facebook posts

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