research-article

Methodological Gaps in Predicting Mental Health States from Social Media: Triangulating Diagnostic Signals

Authors:

Sindhu Kiranmai Ernala,

Michael L. Birnbaum,

Kristin A. Candan,

William A. Sterling,

John M. Kane, and

Munmun De ChoudhuryAuthors Info & Claims

CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

May 2019

Paper No.: 134, Pages 1 - 16

https://doi.org/10.1145/3290605.3300364

Published: 02 May 2019 Publication History

Abstract

A growing body of research is combining social media data with machine learning to predict mental health states of individuals. An implication of this research lies in informing evidence-based diagnosis and treatment. However, obtaining clinically valid diagnostic information from sensitive patient populations is challenging. Consequently, researchers have operationalized characteristic online behaviors as "proxy diagnostic signals" for building these models. This paper posits a challenge in using these diagnostic signals, purported to support clinical decision-making. Focusing on three commonly used proxy diagnostic signals derived from social media, we find that predictive models built on these data, although offer strong internal validity, suffer from poor external validity when tested on mental health patients. A deeper dive reveals issues of population and sampling bias, as well as of uncertainty in construct validity inherent in these proxies. We discuss the methodological and clinical implications of these gaps and provide remedial guidelines for future research.

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Rutter Lten Thij MLorenzo-Luaces LValdez DBollen J(2024)Negative affect variability differs between anxiety and depression on social mediaPLOS ONE10.1371/journal.pone.027210719:2(e0272107)Online publication date: 21-Feb-2024
https://doi.org/10.1371/journal.pone.0272107
Yoo DWoo HPendse SLu NBirnbaum MAbowd GDe Choudhury M(2024)Missed Opportunities for Human-Centered AI Research: Understanding Stakeholder Collaboration in Mental Health AI ResearchProceedings of the ACM on Human-Computer Interaction10.1145/36373728:CSCW1(1-24)Online publication date: 26-Apr-2024
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Index Terms

Methodological Gaps in Predicting Mental Health States from Social Media: Triangulating Diagnostic Signals
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
2. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social media

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cover image ACM Conferences

CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

May 2019

9077 pages

ISBN:9781450359702

DOI:10.1145/3290605

General Chairs:
Stephen Brewster
University of Glasgow, Scotland, UK
,
Geraldine Fitzpatrick
TU Wien, Austria
,
Program Chairs:
Anna Cox
University College London, UK
,
Vassilis Kostakos
University of Melbourne, Australia

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 May 2019

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Research-article

Funding Sources

National Institutes of Health

Conference

CHI '19

Sponsor:

SIGCHI

CHI '19: CHI Conference on Human Factors in Computing Systems

May 4 - 9, 2019

Glasgow, Scotland Uk

Acceptance Rates

CHI '19 Paper Acceptance Rate 703 of 2,958 submissions, 24%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

Upcoming Conference

CHI PLAY '24

Sponsor:
sigchi

The Annual Symposium on Computer-Human Interaction in Play

October 14 - 17, 2024

Tampere , Finland

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https://doi.org/10.1371/journal.pone.0272107
Yoo DWoo HPendse SLu NBirnbaum MAbowd GDe Choudhury M(2024)Missed Opportunities for Human-Centered AI Research: Understanding Stakeholder Collaboration in Mental Health AI ResearchProceedings of the ACM on Human-Computer Interaction10.1145/36373728:CSCW1(1-24)Online publication date: 26-Apr-2024
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Santos Wde Oliveira RParaboni I(2024)SetembroBR: a social media corpus for depression and anxiety disorder predictionLanguage Resources and Evaluation10.1007/s10579-022-09633-058:1(273-300)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s10579-022-09633-0
Yang EKornfield RLiu YChih MSarma PGustafson DCurtin JShah D(2023)Using Machine Learning of Online Expression to Explain Recovery Trajectories: Content Analytic Approach to Studying a Substance Use Disorder ForumJournal of Medical Internet Research10.2196/4558925(e45589)Online publication date: 22-Aug-2023
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Di Cara NMaggio VDavis OHaworth C(2023)Methodologies for Monitoring Mental Health on Twitter: Systematic ReviewJournal of Medical Internet Research10.2196/4273425(e42734)Online publication date: 8-May-2023
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Chancellor SFeuston JChang J(2023)Contextual Gaps in Machine Learning for Mental Illness Prediction: The Case of Diagnostic DisclosuresProceedings of the ACM on Human-Computer Interaction10.1145/36101817:CSCW2(1-27)Online publication date: 4-Oct-2023
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Ajmani LChancellor SMehta BFiesler CZimmer MDe Choudhury M(2023)A Systematic Review of Ethics Disclosures in Predictive Mental Health ResearchProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594082(1311-1323)Online publication date: 12-Jun-2023
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Lee DSon SJeon HKim SHan JSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask LearningProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599917(4357-4369)Online publication date: 6-Aug-2023
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