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.
“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]
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Aragón, M. E., López-Monroy, A. P., González, L. C., & Montes-y-Gómez, M. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3075638 · Cited by 73 · see citing works
Detecting Adolescent Psychological Pressures from Micro-Blog
Xue, Y., Li, Q. L., Li, J., Feng, L., Clifton, D. A., & Clifford, G. D. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-06269-3_10 · Cited by 72 · see citing works
Correia, R. B., Li, L., & Rocha, L. M. (2015) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814749411_0045 · Cited by 72 · see citing works
Pruksachatkun, Y., Pendse, S. R., & Sharma, A. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300294 · Cited by 71 · see citing works
Burkhardt, H. A., Alexopoulos, G. S., Pullmann, M. D., Hull, T. D., Areán, P. A., & Cohen, T. (2021) · Journal of Medical Internet Research · doi:10.2196/28244 · Cited by 68 · see citing works
Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence
Adikari, A., Nawaratne, R., Silva, D. D., Ranasinghe, S., Alahakoon, O., & Alahakoon, D. (2021) · Journal of Medical Internet Research · doi:10.2196/27341 · Cited by 70 · see citing works
Studying the Dark Triad of Personality through Twitter Behavior
Preoțiuc-Pietro, D., Carpenter, J. M., Giorgi, S., & Ungar, L. (2016) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2983323.2983822 · Cited by 68 · see citing works
Mining Social Media Data for Biomedical Signals and Health-Related Behavior
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Searching for Mental Health: A Mixed-Methods Study of Young People's Online Help-seeking
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Is There an App for That?: Ethical Issues in the Digital Mental Health Response to COVID-19
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Cost-Sensitive Boosting Pruning Trees for Depression Detection on Twitter
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Social Monitoring for Public Health
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Trade-offs between machine learning and deep learning for mental illness detection on social media
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Detecting presence of PTSD using sentiment analysis from text data
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Advancing Artificial Intelligence in Health Settings Outside the Hospital and Clinic
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EmoDet2: Emotion Detection in English Textual Dialogue using BERT and BiLSTM Models
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Use of social network sites among depressed adolescents
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Social structure and depression in TrevorSpace
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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
- 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
- Social Sciences › Psychology › Experimental and Cognitive Psychology › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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