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
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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“Counting words belonging to semantic and syntactic categories is simple yet effective, as demonstrated in countless experiments across several fields [14,20,22,24,43,44].”
From Behavioral Activation and Depression Symptomatology: Longitudinal Assessment of Linguistic Indicators in Text-Based Therapy Sessions (2021) · cites this paper as [43]
Citations
Cites
The CES-D Scale: A Self-Report Depression Scale for Research in the General Population
Radloff, L. S. (1977) · Applied Psychological Measurement · doi:10.1177/014662167700100306 · Cited by 53,267
Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,482
Projections of Global Mortality and Burden of Disease from 2002 to 2030
Mathers, C. D., & Loncar, D. (2006) · PLoS Medicine · doi:10.1371/journal.pmed.0030442 · Cited by 11,386
Kessler, R. C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K. R., Rush, A. J., Walters, E. E., & Wang, P. S. (2003) · JAMA · doi:10.1001/jama.289.23.3095 · Cited by 7,988
Kawachi, I. I. (2001) · Journal of Heredity · doi:10.1093/jurban/78.3.458 · Cited by 3,565
Pattern Classification (2nd Edition)
Duda, R. O., Hart, P. E., & Stork, D. G. (2000) · Wiley-Interscience eBooks · Cited by 2,070
Contact With Mental Health and Primary Care Providers Before Suicide: A Review of the Evidence
Luoma, J. B., Martin, C. E., & Pearson, J. L. (2002) · American Journal of Psychiatry · doi:10.1176/appi.ajp.159.6.909 · Cited by 1,576
Coping, stress, and social resources among adults with unipolar depression.
Billings, A. G., & Moos, R. H. (1984) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.46.4.877 · Cited by 1,157
Language use of depressed and depression-vulnerable college students
Rude, S., Gortner, E.-M., & Pennebaker, J. (2004) · Cognition & Emotion · doi:10.1080/02699930441000030 · Cited by 1,018
You Are What You Tweet: Analyzing Twitter for Public Health
Paul, M., & Dredze, M. H. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14137 · Cited by 946
Research works printed but not linked: 3
- 10.5555/954544 · DOI
- 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.
- 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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Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning
Mohr, D. C., Zhang, M., & Schueller, S. M. (2017) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-044949 · Cited by 802
Social Networking Sites, Depression, and Anxiety: A Systematic Review
Seabrook, E. M., Kern, M. L., & Rickard, N. S. (2016) · JMIR Mental Health · doi:10.2196/mental.5842 · Cited by 763
“For instance, De Choudhury et al [91] demonstrated that the volume of tweets and the associated replies were reduced in Twitter users with depression compared with those without.”
InfluentialQuantifying Mental Health Signals in Twitter
Coppersmith, G., Dredze, M., & Harman, C. (2014) · Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality · doi:10.3115/v1/w14-3207 · Cited by 722
Methods in predictive techniques for mental health status on social media: a critical review
Chancellor, S., & De Choudhury, M. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0233-7 · Cited by 557
Predicting the Big 5 personality traits from digital footprints on social media: A meta-analysis
Azucar, D., Marengo, D., & Settanni, M. (2017) · Personality and Individual Differences · doi:10.1016/j.paid.2017.12.018 · Cited by 572
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
- 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
- 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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