Artificial intelligence applications in social media for depression screening: A systematic review protocol for content validity processes.

<h4>Background</h4>The popularization of social media has led to the coalescing of user groups around mental health conditions; in particular, depression. Social media offers a rich environment for contextualizing and predicting users' self-reported burden of depression. Modern arti...

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Autores principales:	Priscilla N Owusu, Ulrich Reininghaus, Georgia Koppe, Irene Dankwa-Mullan, Till Bärnighausen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1e19492385df46efa748c94e3fd0b1fa
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spelling	oai:doaj.org-article:1e19492385df46efa748c94e3fd0b1fa2021-12-02T20:05:59ZArtificial intelligence applications in social media for depression screening: A systematic review protocol for content validity processes.1932-620310.1371/journal.pone.0259499https://doaj.org/article/1e19492385df46efa748c94e3fd0b1fa2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259499https://doaj.org/toc/1932-6203<h4>Background</h4>The popularization of social media has led to the coalescing of user groups around mental health conditions; in particular, depression. Social media offers a rich environment for contextualizing and predicting users' self-reported burden of depression. Modern artificial intelligence (AI) methods are commonly employed in analyzing user-generated sentiment on social media. In the forthcoming systematic review, we will examine the content validity of these computer-based health surveillance models with respect to standard diagnostic frameworks. Drawing from a clinical perspective, we will attempt to establish a normative judgment about the strengths of these modern AI applications in the detection of depression.<h4>Methods</h4>We will perform a systematic review of English and German language publications from 2010 to 2020 in PubMed, APA PsychInfo, Science Direct, EMBASE Psych, Google Scholar, and Web of Science. The inclusion criteria span cohort, case-control, cross-sectional studies, randomized controlled studies, in addition to reports on conference proceedings. The systematic review will exclude some gray source materials, specifically editorials, newspaper articles, and blog posts. Our primary outcome is self-reported depression, as expressed on social media. Secondary outcomes will be the types of AI methods used for social media depression screen, and the clinical validation procedures accompanying these methods. In a second step, we will utilize the evidence-strengthening Population, Intervention, Comparison, Outcomes, Study type (PICOS) tool to refine our inclusion and exclusion criteria. Following the independent assessment of the evidence sources by two authors for the risk of bias, the data extraction process will culminate in a thematic synthesis of reviewed studies.<h4>Discussion</h4>We present the protocol for a systematic review which will consider all existing literature from peer reviewed publication sources relevant to the primary and secondary outcomes. The completed review will discuss depression as a self-reported health outcome in social media material. We will examine the computational methods, including AI and machine learning techniques which are commonly used for online depression surveillance. Furthermore, we will focus on standard clinical assessments, as indicating content validity, in the design of the algorithms. The methodological quality of the clinical construct of the algorithms will be evaluated with the COnsensus-based Standards for the selection of health status Measurement Instruments (COSMIN) framework. We conclude the study with a normative judgment about the current application of AI to screen for depression on social media.<h4>Systematic review registration</h4>International Prospective Register of Systematic Reviews PROSPERO (registration number CRD42020187874).Priscilla N OwusuUlrich ReininghausGeorgia KoppeIrene Dankwa-MullanTill BärnighausenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0259499 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Priscilla N Owusu Ulrich Reininghaus Georgia Koppe Irene Dankwa-Mullan Till Bärnighausen Artificial intelligence applications in social media for depression screening: A systematic review protocol for content validity processes.
description	<h4>Background</h4>The popularization of social media has led to the coalescing of user groups around mental health conditions; in particular, depression. Social media offers a rich environment for contextualizing and predicting users' self-reported burden of depression. Modern artificial intelligence (AI) methods are commonly employed in analyzing user-generated sentiment on social media. In the forthcoming systematic review, we will examine the content validity of these computer-based health surveillance models with respect to standard diagnostic frameworks. Drawing from a clinical perspective, we will attempt to establish a normative judgment about the strengths of these modern AI applications in the detection of depression.<h4>Methods</h4>We will perform a systematic review of English and German language publications from 2010 to 2020 in PubMed, APA PsychInfo, Science Direct, EMBASE Psych, Google Scholar, and Web of Science. The inclusion criteria span cohort, case-control, cross-sectional studies, randomized controlled studies, in addition to reports on conference proceedings. The systematic review will exclude some gray source materials, specifically editorials, newspaper articles, and blog posts. Our primary outcome is self-reported depression, as expressed on social media. Secondary outcomes will be the types of AI methods used for social media depression screen, and the clinical validation procedures accompanying these methods. In a second step, we will utilize the evidence-strengthening Population, Intervention, Comparison, Outcomes, Study type (PICOS) tool to refine our inclusion and exclusion criteria. Following the independent assessment of the evidence sources by two authors for the risk of bias, the data extraction process will culminate in a thematic synthesis of reviewed studies.<h4>Discussion</h4>We present the protocol for a systematic review which will consider all existing literature from peer reviewed publication sources relevant to the primary and secondary outcomes. The completed review will discuss depression as a self-reported health outcome in social media material. We will examine the computational methods, including AI and machine learning techniques which are commonly used for online depression surveillance. Furthermore, we will focus on standard clinical assessments, as indicating content validity, in the design of the algorithms. The methodological quality of the clinical construct of the algorithms will be evaluated with the COnsensus-based Standards for the selection of health status Measurement Instruments (COSMIN) framework. We conclude the study with a normative judgment about the current application of AI to screen for depression on social media.<h4>Systematic review registration</h4>International Prospective Register of Systematic Reviews PROSPERO (registration number CRD42020187874).
format	article
author	Priscilla N Owusu Ulrich Reininghaus Georgia Koppe Irene Dankwa-Mullan Till Bärnighausen
author_facet	Priscilla N Owusu Ulrich Reininghaus Georgia Koppe Irene Dankwa-Mullan Till Bärnighausen
author_sort	Priscilla N Owusu
title	Artificial intelligence applications in social media for depression screening: A systematic review protocol for content validity processes.
title_short	Artificial intelligence applications in social media for depression screening: A systematic review protocol for content validity processes.
title_full	Artificial intelligence applications in social media for depression screening: A systematic review protocol for content validity processes.
title_fullStr	Artificial intelligence applications in social media for depression screening: A systematic review protocol for content validity processes.
title_full_unstemmed	Artificial intelligence applications in social media for depression screening: A systematic review protocol for content validity processes.
title_sort	artificial intelligence applications in social media for depression screening: a systematic review protocol for content validity processes.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/1e19492385df46efa748c94e3fd0b1fa
work_keys_str_mv	AT priscillanowusu artificialintelligenceapplicationsinsocialmediafordepressionscreeningasystematicreviewprotocolforcontentvalidityprocesses AT ulrichreininghaus artificialintelligenceapplicationsinsocialmediafordepressionscreeningasystematicreviewprotocolforcontentvalidityprocesses AT georgiakoppe artificialintelligenceapplicationsinsocialmediafordepressionscreeningasystematicreviewprotocolforcontentvalidityprocesses AT irenedankwamullan artificialintelligenceapplicationsinsocialmediafordepressionscreeningasystematicreviewprotocolforcontentvalidityprocesses AT tillbarnighausen artificialintelligenceapplicationsinsocialmediafordepressionscreeningasystematicreviewprotocolforcontentvalidityprocesses
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Artificial intelligence applications in social media for depression screening: A systematic review protocol for content validity processes.

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