Cited by 497Mental Health via WritingSentiment Analysis and Opinion MiningEmotion and Mood Recognition

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Social media as a measurement tool of depression in populations

De Choudhury, Munmun, Counts, Scott, Horvitz, Eric·2013·Proceedings of the 5th Annual ACM Web Science Conference

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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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Authors: De Choudhury, M., Counts, S., & Horvitz, E.
Affiliations: Microsoft Research, Redmond WA
Microsoft (United States) (US)
ORCID: Horvitz, Eric
Venue: Proceedings of the 5th Annual ACM Web Science Conference
Publisher: ACM
Published: 2013-05-02
Pages: 47-56
DOI: 10.1145/2464464.2464480
OpenAlex: W2106686523
Cited by: 497
References: 24
Citations by year: 2026: 13 · 2025: 26 · 2024: 23 · 2023: 50 · 2022: 57 · 2021: 61 · 2020: 64 · 2019: 62 · 2018: 30 · 2017: 43 · 2016: 32 · 2015: 20 · 2014: 14 · 2013: 1
FWCI: 45.03
Type: conference paper
Language: en
Open access: closed
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: ACM Web Science Conference (WebSci) (publishedVersion)
Topics: Social Sciences › Psychology › Social Psychology › Mental Health via Writing
Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Keywords: depression, social media, Twitter, crowdsourcing, major depressive disorder, probabilistic classification, social activity, emotion recognition, linguistic analysis, geographic patterns, seasonal patterns, demographic patterns, large-scale measurement

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Social media as a measurement tool of depression in populations

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