Methods in predictive techniques for mental health status on social media: a critical review

Abstract Social media is now being used to model mental well-being, and for understanding health outcomes. Computer scientists are now using quantitative techniques to predict the presence of specific mental disorders and symptomatology, such as depression, suicidality, and anxiety. This research pr...

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Autores principales:	Stevie Chancellor, Munmun De Choudhury
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/9ec6ccee175443fb87125d4dbc61ab45
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spelling	oai:doaj.org-article:9ec6ccee175443fb87125d4dbc61ab452021-12-02T11:45:05ZMethods in predictive techniques for mental health status on social media: a critical review10.1038/s41746-020-0233-72398-6352https://doaj.org/article/9ec6ccee175443fb87125d4dbc61ab452020-03-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0233-7https://doaj.org/toc/2398-6352Abstract Social media is now being used to model mental well-being, and for understanding health outcomes. Computer scientists are now using quantitative techniques to predict the presence of specific mental disorders and symptomatology, such as depression, suicidality, and anxiety. This research promises great benefits to monitoring efforts, diagnostics, and intervention design for these mental health statuses. Yet, there is no standardized process for evaluating the validity of this research and the methods adopted in the design of these studies. We conduct a systematic literature review of the state-of-the-art in predicting mental health status using social media data, focusing on characteristics of the study design, methods, and research design. We find 75 studies in this area published between 2013 and 2018. Our results outline the methods of data annotation for mental health status, data collection and quality management, pre-processing and feature selection, and model selection and verification. Despite growing interest in this field, we identify concerning trends around construct validity, and a lack of reflection in the methods used to operationalize and identify mental health status. We provide some recommendations to address these challenges, including a list of proposed reporting standards for publications and collaboration opportunities in this interdisciplinary space.Stevie ChancellorMunmun De ChoudhuryNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Stevie Chancellor Munmun De Choudhury Methods in predictive techniques for mental health status on social media: a critical review
description	Abstract Social media is now being used to model mental well-being, and for understanding health outcomes. Computer scientists are now using quantitative techniques to predict the presence of specific mental disorders and symptomatology, such as depression, suicidality, and anxiety. This research promises great benefits to monitoring efforts, diagnostics, and intervention design for these mental health statuses. Yet, there is no standardized process for evaluating the validity of this research and the methods adopted in the design of these studies. We conduct a systematic literature review of the state-of-the-art in predicting mental health status using social media data, focusing on characteristics of the study design, methods, and research design. We find 75 studies in this area published between 2013 and 2018. Our results outline the methods of data annotation for mental health status, data collection and quality management, pre-processing and feature selection, and model selection and verification. Despite growing interest in this field, we identify concerning trends around construct validity, and a lack of reflection in the methods used to operationalize and identify mental health status. We provide some recommendations to address these challenges, including a list of proposed reporting standards for publications and collaboration opportunities in this interdisciplinary space.
format	article
author	Stevie Chancellor Munmun De Choudhury
author_facet	Stevie Chancellor Munmun De Choudhury
author_sort	Stevie Chancellor
title	Methods in predictive techniques for mental health status on social media: a critical review
title_short	Methods in predictive techniques for mental health status on social media: a critical review
title_full	Methods in predictive techniques for mental health status on social media: a critical review
title_fullStr	Methods in predictive techniques for mental health status on social media: a critical review
title_full_unstemmed	Methods in predictive techniques for mental health status on social media: a critical review
title_sort	methods in predictive techniques for mental health status on social media: a critical review
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/9ec6ccee175443fb87125d4dbc61ab45
work_keys_str_mv	AT steviechancellor methodsinpredictivetechniquesformentalhealthstatusonsocialmediaacriticalreview AT munmundechoudhury methodsinpredictivetechniquesformentalhealthstatusonsocialmediaacriticalreview
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Methods in predictive techniques for mental health status on social media: a critical review

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