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

The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.

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
DOI 10.1145/2464464.2464480
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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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Research works printed but not linked: 3

  1. 10.5555/954544 · DOI
  2. Krieck, M., Dreesman, J., Otrusina, L., & Denecke, K. (2011). A new age of public health: Identifying disease outbreaks by analyzing tweets. In Proc. Health Web-Science Workshop, ACM Web Science Conference.
  3. Park, M., Cha, C., & Cha, M. (2012). Depressive Moods of Users Captured in Twitter. In Proc. ACM SIGKDD Workshop on Healthcare Informatics (HI-KDD).

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Metadata

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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