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...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Yaakov Ophir, Refael Tikochinski, Christa S. C. Asterhan, Itay Sisso, Roi Reichart
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
Materias:
R
Q
Acceso en línea:https://doaj.org/article/f2eff099f81a47a5be67597ff0d20008
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:f2eff099f81a47a5be67597ff0d20008
record_format dspace
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
_version_ 1718377896383348736