research-article

Social media as a measurement tool of depression in populations

Authors:

Munmun De Choudhury,

Eric HorvitzAuthors Info & Claims

WebSci '13: Proceedings of the 5th Annual ACM Web Science Conference

Pages 47 - 56

https://doi.org/10.1145/2464464.2464480

Published: 02 May 2013 Publication History

Abstract

Depression is a serious and widespread public health challenge. We examine the potential for leveraging social media postings as a new type of lens in understanding depression in populations. Information gleaned from social media bears potential to complement traditional survey techniques in its ability to provide finer grained measurements over time while radically expanding population sample sizes. We present work on using a crowdsourcing methodology to build a large corpus of postings on Twitter that have been shared by individuals diagnosed with clinical depression. Next, we develop a probabilistic model trained on this corpus to determine if posts could indicate depression. The model leverages signals of social activity, emotion, and language manifested on Twitter. Using the model, we introduce a social media depression index that may serve to characterize levels of depression in populations. Geographical, demographic and seasonal patterns of depression given by the measure confirm psychiatric findings and correlate highly with depression statistics reported by the Centers for Disease Control and Prevention (CDC).

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Cited By

Nova FShinde APfafman RHarrison ALogan Delaney CPater J(2025)Sentiment Analysis of #Meanspo Tweets: Humans vs. Automatic ClassificationsProceedings of the ACM on Human-Computer Interaction10.1145/37012079:1(1-26)Online publication date: 10-Jan-2025
https://dl.acm.org/doi/10.1145/3701207
Dalal SJain SDave M(2025)Review of Advancements in Depression Detection Using Social Media DataIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.344862412:1(77-100)Online publication date: Mar-2025
https://doi.org/10.1109/TCSS.2024.3448624
Tahir WKhalid SAlmutairi SAbohashrh MMemon SKhan J(2025)Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning TechniquesIEEE Access10.1109/ACCESS.2025.353086213(12789-12818)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3530862
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Index Terms

Social media as a measurement tool of depression in populations
1. Human-centered computing

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Published In

cover image ACM Conferences

WebSci '13: Proceedings of the 5th Annual ACM Web Science Conference

May 2013

481 pages

ISBN:9781450318891

DOI:10.1145/2464464

Conference Chairs:
Hugh Davis
University of Southampton
,
Harry Halpin
World Wide Web Consortium
,
Alex Pentland,
Program Chairs:
Mark Bernstein,
Lada Adamic,
Harith Alani,
Alexandre Monnin,
Richard Rogers

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 May 2013

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WebSci '13: Web Science 2013

May 2 - 4, 2013

Paris, France

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Overall Acceptance Rate 245 of 933 submissions, 26%

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Cited By

Nova FShinde APfafman RHarrison ALogan Delaney CPater J(2025)Sentiment Analysis of #Meanspo Tweets: Humans vs. Automatic ClassificationsProceedings of the ACM on Human-Computer Interaction10.1145/37012079:1(1-26)Online publication date: 10-Jan-2025
https://dl.acm.org/doi/10.1145/3701207
Dalal SJain SDave M(2025)Review of Advancements in Depression Detection Using Social Media DataIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.344862412:1(77-100)Online publication date: Mar-2025
https://doi.org/10.1109/TCSS.2024.3448624
Tahir WKhalid SAlmutairi SAbohashrh MMemon SKhan J(2025)Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning TechniquesIEEE Access10.1109/ACCESS.2025.353086213(12789-12818)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3530862
Couto MPerez AParapar JLosada D(2025)Temporal Word Embeddings for Early Detection of Psychological Disorders on Social MediaJournal of Healthcare Informatics Research10.1007/s41666-025-00186-9Online publication date: 22-Jan-2025
https://doi.org/10.1007/s41666-025-00186-9
Babkova NHuliieva DKochuieva ZUgolnikova N(2024)ANALYSIS OF MENTAL HEALTH RESEARCHGrail of Science10.36074/grail-of-science.16.02.2024.037(229-236)Online publication date: 24-Feb-2024
https://doi.org/10.36074/grail-of-science.16.02.2024.037
Owen DLynham ASmart SPardiñas ACamacho Collados J(2024)AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and ChallengesJournal of Medical Internet Research10.2196/5922526(e59225)Online publication date: 15-Nov-2024
https://doi.org/10.2196/59225
Xu XYao BDong YGabriel SYu HHendler JGhassemi MDey AWang D(2024)Mental-LLMProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435408:1(1-32)Online publication date: 6-Mar-2024
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Prama TIslam MAnwar MJahan I(2024)AI-Enabled Deep Depression Detection and Evaluation Informed by DSM-5-TRIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.338213911:5(6453-6465)Online publication date: Oct-2024
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Lee JYou JLee D(2024)User Preferences on AI Psychotherapy Based on Moderating Effects of Individual Personality Traits: Employing a Clustering AnalysisInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2307695(1-18)Online publication date: 6-Feb-2024
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Hur JHeffner JFeng GJoormann JRutledge R(2024)Language sentiment predicts changes in depressive symptomsProceedings of the National Academy of Sciences10.1073/pnas.2321321121121:39Online publication date: 16-Sep-2024
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