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

Open this paper in CoChat — free account

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).

Integrity signals

Registry

Crossref · 10.1145/2464464.2464480

Cites

26 of 26 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

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
Pattern Classification
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
The Epidemiology of Major Depressive Disorder: Results From the National Comorbidity Survey Replication (NCS-R)
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
Social Ties and Mental Health
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

Flagged first, then by citations

Show all 24 references

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).

Sorted by citations

Items 301–400 of 497

Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data
Giorgi, S., Yaden, D. B., Eichstaedt, J. C., Ungar, L., Schwartz, H. A., Kwarteng, A. E., & Curtis, B. L. (2023) · Scientific Reports · doi:10.1038/s41598-023-34468-2 · Cited by 8
Artificial and Human Intelligence in Mental Health
Sigman, M., Slezak, D. F., Drucaroff, L. J., Ribeiro, S. T. G., & Carrillo, F. (2021) · AI Magazine · doi:10.1002/j.2371-9621.2021.tb00009.x · Cited by 8
Using Depression Analytics to Reduce Stigma via Social Media: BlueFriends
Haimson, O. L., Ringland, K. E., Simpson, S., & Wolf, C. T. (2014) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 8
Adapting language models for mental health analysis on social media
Aragón, M. E., López-Monroy, A. P., Montes-y-Gómez, M., & Losada, D. E. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103217 · Cited by 9
Automated Tracking of Components of Job Satisfaction via Text Mining of Twitter Data
Hickman, L., Saha, K., Choudhury, M. D., & Tay, L. (2019) · Cited by 7
Study about Gathering Features in Depression Detection’ Problem with Health Professionals Community
Filho, S. L., da Silva, M. F., Oliveira, J., & Ruback, L. (2022) · iSys - Brazilian Journal of Information Systems · doi:10.5753/isys.2022.2285 · Cited by 7
#AutismAwareness
Quadri, S., Karusala, N., & Arriaga, R. I. (2018) · Indian Conference on Human-Computer Interaction (IndiaHCI) · doi:10.1145/3297121.3297128 · Cited by 7
An Effective Machine Learning Framework for Data Elements Extraction from the Literature of Anxiety Outcome Measures to Build Systematic Review
Goswami, S., Pal, S., Goldsworthy, S., & Basu, T. (2019) · Lecture notes in business information processing · doi:10.1007/978-3-030-20485-3_19 · Cited by 7
GraphBoot: Quantifying Uncertainty in Node Feature Learning on Large Networks
Akçora, C. G., Gel, Y. R., Kantarcıoğlu, M., Lyubchich, V., & Thuraisingham, B. M. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2925355 · Cited by 7
Media Sosial dan Kecanduan Penggunaan Menurut Islam
Ayub, M. S., Rozali, A. A., & Marni, N. (2019) · Cited by 7
Agent-Based Modeling and Simulation of Depression and Its Impact on Student Success and Academic Retention
Abdelhamid, S. E., Kuhlman, C. J., MARATHE, M. V., Ravi, S., & Reid, K. (2016) · ASEE Annual Conference & Exposition · doi:10.18260/p.26545 · Cited by 7
In your wildest dreams: the language and psychological features of dreams
Niederhoffer, K. G., Schler, J., Crutchley, P., Loveys, K., & Coppersmith, G. (2017) · Workshop on Computational Linguistics and Clinical Psychology (CLPsych) · doi:10.18653/v1/w17-3102 · Cited by 7
Associations Between Affective States and Sexual and Health Status Among Men Who Have Sex With Men in China: Exploratory Study Using Social Media Data
Zheng, Z., Yang, Q., Liu, Z., Qiu, J., Gu, J., Hao, Y., Song, C., Jia, Z., & Hao, C. (2019) · Journal of Medical Internet Research · doi:10.2196/13201 · Cited by 8
Extraction of Depression Symptoms From Social Networks
Chilwal, B., & Mishra, A. K. (2021) · doi:10.1002/9781119761655.ch17 · Cited by 7
Automated Speech Analysis for Screening and Monitoring Bipolar Depression: Machine Learning Model Development and Interpretation Study
Min, S., Yeum, T.-S., Shin, D., Rhee, S. J., Lee, H., Lee, H.-S., Park, S., Lee, J., & Ahn, Y. M. (2025) · JMIR Medical Informatics · doi:10.2196/79093 · Cited by 6
Objective speech measures capture depressive symptoms and associated cognitive difficulties
Wiseman, M., Yep, R., Alexander, M. W., Pople, C. B., Perri, L., Gopinath, G., Vasileiadi, M., Robin, J., Spilka, M. J., Simpson, W. S., et al. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03728-2 · Cited by 8
Development of intelligent system based on synthesis of affective signals and deep neural networks to foster mental health of the Indian virtual community
Arora, M. K., Singh, J., & Singh, A. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-023-01179-5 · Cited by 6
User Preferences on AI Psychotherapy Based on Moderating Effects of Individual Personality Traits: Employing a Clustering Analysis
Lee, J., You, J. S., & Lee, D. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2307695 · Cited by 8
Small cohort of patients with epilepsy showed increased activity on Facebook before sudden unexpected death
Wood, I. B., Correia, R. B., Miller, W. R., & Rocha, L. M. (2022) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2022.108580 · Cited by 6
Developing a Utopian Model of Human-Technology Interaction: Collective Intelligence Applications in Support of Future Well-Being
Soch, N. N., Hogan, M., Harney, O., Hanlon, M. L. D., Brady, C., & McGrattan, L. (2022) · Utopian Studies · doi:10.5325/utopianstudies.33.1.0054 · Cited by 5
Synthesis of Affective Expressions and Artificial Intelligence to Discover Mental Distress in Online Community
Singh, A., & Singh, J. (2022) · International Journal of Mental Health and Addiction · doi:10.1007/s11469-022-00966-z · Cited by 9
Measuring the Stigmatizing Effects of a Highly Publicized Event on Online Mental Health Discourse
Fang, A., & Zhu, H. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581284 · Cited by 6
Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers
Teodorescu, D., Cheng, T., Fyshe, A., & Mohammad, S. M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.188 · Cited by 11
Developing a social sensing index for monitoring place-oriented mental health issues using social media (twitter) data
Park, J., Tsou, M., Nara, A., Cassels, S. L., & Dodge, S. (2024) · Urban Informatics · doi:10.1007/s44212-023-00033-5 · Cited by 8
Analysis of Post centric suicidal expressions and classification on the Social Media Post: Twitter
Lalrinmawii, C., Vanlalhruaia, & Debnath, S. (2020) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt49239.2020.9225638 · Cited by 6
User Centric Social Opinion and Clinical Behavioural Model for Depression Detection
Ibitoye, A., Famutimi, R. F., Olanloye, O. D., & Akioyamen, E. (2021) · International Journal of Intelligent Information Systems · doi:10.11648/j.ijiis.20211004.15 · Cited by 6
The Problem of Semantic Shift in Longitudinal Monitoring of Social Media
Harrigian, K., & Dredze, M. H. (2022) · ACM Web Science Conference (WebSci) · doi:10.1145/3501247.3531566 · Cited by 9
An empirical study on prediction of population health through social media
Nguyen, H. S., Nguyen, T., & Nguyen, D. T. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103277 · Cited by 6
On Text Preprocessing for Early Detection of Depression on Social Media
Figuerêdo, J. S. L., & Calumby, R. T. (2020) · doi:10.5753/sbcas.2020.11504 · Cited by 6
Effect of social capital on emotion, language style and latent topics in online depression community
Dao, B., Nguyen, T., Venkatesh, S., & Phung, D. (2016) · RIVF International Conference on Computing and Communication Technologies (RIVF) · doi:10.1109/rivf.2016.7800270 · Cited by 9
A rare allergic reaction to sodium methohexital.
Reichert, E. F., & Bassett, P. A. (1972) · PubMed · Cited by 6
Individual-level Anxiety Detection and Prediction from Longitudinal YouTube and Google Search Engagement LogsPreprint
Zaman, A., Zhang, B., Silenzio, V., Hoque, M. E., & Kautz, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00613 · Cited by 8
A Social Media Study of Portrayals of Bipolar Disorders on YouTube: Content and Thematic Analyses
Mayor, E., & Bietti, L. M. (2025) · Journal of Medical Internet Research · doi:10.2196/67129 · Cited by 5
Jointly Predicting Affective and Mental Health Scores Using Deep Neural Networks of Visual Cues on the Web
Nguyen, H., Nguyen, V., Nguyen, T., Larsen, M. E., O’Dea, B., Nguyen, D. T., Le, T., Phung, D., Venkatesh, S., & Christensen, H. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-02925-8_7 · Cited by 12
Approaching what and how people with mental disorders communicate in social media–Introducing a multi-channel representation
Aragón, M. E., López-Monroy, A. P., González, L. C., & Montes-y-Gómez, M. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07569-8 · Cited by 5
Using Machine Learning of Online Expression to Explain Recovery Trajectories: Content Analytic Approach to Studying a Substance Use Disorder Forum
Yang, E. F., Kornfield, R., Liu, Y., Chih, M., Sarma, P. K., Gustafson, D. H., Curtin, J. J., & Shah, D. V. (2023) · Journal of Medical Internet Research · doi:10.2196/45589 · Cited by 5
Privacy protection and utility trade-off for social graph embedding
Cai, L., Tang, J., Dang, S., & Chen, G. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120866 · Cited by 5
Psychological Stress Detection from Online Shopping
Zhao, L., Wang, H., Xue, Y., Li, Q., & Feng, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45814-4_35 · Cited by 5
Mental Health Analysis of Students in Major Cities of India During COVID-19
Agarwal, A., Agarwal, B., Harjule, P., & Agarwal, A. K. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-33-4236-1_4 · Cited by 5
Social Media is not a Health Proxy: Differences Between Social Media and Electronic Health Record Reports of Post-COVID Symptoms
Pater, J. A., Coupe, A., Nova, F. F., Pfafman, R., Carroll, J., Brouwer, A., Bohn, C., Li, J., Todd, N., Chang, F. L., et al. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579624 · Cited by 9
Resolution of grammatical tense into actual time, and its application in Time Perspective study in the tweet space
Kamila, S., Hasanuzzaman, M., Ekbal, A., & Bhattacharyya, P. (2019) · PLoS ONE · doi:10.1371/journal.pone.0211872 · Cited by 6
Bridging techniques and applications in sentiment analysis: Approaches, challenges, and emerging trends
Deori, S., Chutia, T., Saikia, H. J., Boro, A., & Baruah, N. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113255 · Cited by 5
End-to-end multimodal system for depression detection from online recordings
Kowalewski, M., Stroinski, M., Kwarciak, K., Laptiev, V., & Hemmerling, D. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10340782 · Cited by 4
Exploring Social Media's Role in Counseling
Thandayuthapani, S. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-8392-6.ch005 · Cited by 5
The Effect of Photoperiod on the Mood of Reddit Users
Ethayarajh, K., & Rudzicz, F. (2017) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2016.0487 · Cited by 4
Engaging Smartphones and Social Data for Curing Depressive Disorders: An Overview and Survey
Bhatia, S., Kesarwani, Y., Basantani, A., & Jain, S. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-15-7533-4_64 · Cited by 4
A Psychosocial Perspective about Mental Health and League of Legends in Brazil
Carvalho, L. P., Suzano, J. A., Gonçalvez, I., Filho, S. L., Santoro, F. M., & Oliveira, J. (2021) · Journal on Interactive Systems · doi:10.5753/jis.2021.1896 · Cited by 4
Detection of Depression and Suicidal Ideation on Social Media: An Intrinsic Review
Madkar, S., Maheshwari, T., Merani, M., Merchant, R., & Doshi, P. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-88244-0_7 · Cited by 4
EmoMix+: An Approach of Depression Detection Based on Emotion Lexicon for Mobile Application
Li, R., Zhang, Y., Yin, L., Sun, Z., Lin, Z., Fu, P., Wang, W., & Shi, G. (2022) · Security and Communication Networks · doi:10.1155/2022/1208846 · Cited by 4
Affective Computing for Brain Health Disorders
Smith, E., Storch, E. A., Lavretsky, H., Cummings, J. L., & Eyre, H. A. (2023) · Springer eBooks · doi:10.1007/978-3-319-75922-7_36 · Cited by 4
Unpacking the exploration–exploitation tradeoff on Snapchat: The relationships between users’ exploration–exploitation interests and server log data
Gómez-Zará, D., Liu, Y., Neves, L., Shah, N., & Bos, M. W. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.108014 · Cited by 6
LTL-INAOE's Participation at eRisk 2019: Detecting Anorexia in Social Media through Shared Personal Information.
Ortega-Mendoza, R. M., Farías, D. I. H., & Montes-y-Gómez, M. (2019) · CLEF (Working Notes) · Cited by 4
Using Arabic Tweets to Understand Drug Selling Behaviors
Alruwaili, W., Protano, B., Sirigiriraju, T., & Alhoori, H. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.12.220 · Cited by 4
Estimating County Health Indices Using Graph Neural Networks
Nguyen, H., Nguyen, D. T., & Nguyen, T. (2019) · Communications in computer and information science · doi:10.1007/978-981-15-1699-3_6 · Cited by 4
Media Sosial dan Wanita Menurut Islam
Nazir, M., Noh, M. I. Z. C., & Basiron, B. B. (2019) · Cited by 4
Automatic detection of depression from text data
Magami, F. M., & Digiampietri, L. A. (2020) · Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI) · doi:10.1145/3411564.3411603 · Cited by 4
Examining the Relationship Between Songs and Psychological Characteristics
Pyun, M., Kim, D., Lim, C., Lee, E., Kwon, J., & Lee, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60128-7_8 · Cited by 3
Multiple-Choice Question Answering Models for Automatic Depression Severity Estimation
Gabín, J., Pérez, A., & Parapar, J. (2021) · XoveTIC Conference (XoveTIC) · doi:10.3390/engproc2021007023 · Cited by 3
Applying Machine Learning to Detect Depression-Related Texts on Social Networks
Shekerbekova, S. T., Yerekesheva, M., Tukenova, L., Turganbay, K., Kozhamkulova, Z., & Omarov, B. S. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-3660-8_15 · Cited by 3
Predicting Depression Using Social Media Posts
Bakar, F. A., Nawi, N. M., & Hezam, A. A. (2021) · Journal of Soft Computing and Data Mining · doi:10.30880/jscdm.2021.02.02.004 · Cited by 3
PsyProf: A Platform for Assisted Screening of Depression in Social Media
Pérez, A., Piot-Pérez-Abadín, P., Parapar, J., & Barreiro, Á. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-28241-6_30 · Cited by 3
A Cross-Modal Study of Pain Across Communities in the United States
Aggarwal, A., Rai, S., Giorgi, S., Havaldar, S., Sherman, G. T., Mittal, J., & Guntuku, S. C. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543873.3587642 · Cited by 3
Media Sosial dan Facebook Menurut Islam
Haronzah, M. F., Mani, I., & Embong, W. H. W. (2019) · UTM Institutional Repository · Cited by 3
Extracting Socioeconomic Patterns from the News: Modelling Text and Outlet Importance Jointly
Lampos, V., Preoțiuc-Pietro, D., Samangooei, S., Gelling, D., & Cohn, T. (2014) · doi:10.3115/v1/w14-2507 · Cited by 3
Discovering latent affective dynamics among individuals in online mental health-related communities
Dao, B., Nguyen, T., Venkatesh, S., & Phung, D. (2016) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2016.7552947 · Cited by 3
Learning the student's sufferings using Social Networks
Upreti, M., & Kumar, V. (2017) · International Conference on Computing, Communication and Automation · doi:10.1109/ccaa.2017.8229824 · Cited by 3
Stress Level Estimation among Students via Social Media Patterns Using Hybrid Machine Learning and Neural Architectures
Khot, B. A. P., Murgod, P. S., Ittannavar, S. S., Hosamani, R., Hiremath, S. R., & Arabhanvi, A. B. (2025) · International Conference on Smart & Sustainable Technology (INCSST) · doi:10.1109/incsst64791.2025.11210377 · Cited by 2
Capsule-augmented deep learning architectures for mental health detection from social media text
Wagay, F. A., & Jahiruddin (2025) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2025.100319 · Cited by 3
Symptoms and Their Temporal Distribution: An Interpretable AI Approach for Depression Detection on Social Media1
Kuang, J., Xie, J., & Yan, Z. (2025) · MIS Quarterly · doi:10.25300/misq/2025/18897 · Cited by 3
Complementing Studies on Vulnerable Youths with Reddit Data
Mauri, A., Psyllidis, A., Bozzon, A., Lee, J.-., Pridmore, J., van Zoonen, L., & Giest, S. N. (2021) · Biannual Conference of the Italian SIGCHI Chapter (CHItaly) · doi:10.1145/3464385.3464703 · Cited by 2
Substance use and sentiment and topical tendencies: a study using social media conversations of youth experiencing homelessness
Deng, T., Barman‐Adhikari, A., Lee, Y. J., Dewri, R., & Bender, K. (2022) · Information Technology and People · doi:10.1108/itp-12-2020-0860 · Cited by 2
Performance of Content-Based Features to Detect Depression Tendencies in Different Text Lengths
Zulkarnain, N. Z., Yusof, N. F. A., Ahmad, S. S. S., Othman, Z. B., & Hashim, A. H. (2022) · IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) · doi:10.1109/iicaiet55139.2022.9936811 · Cited by 2
Exploring Suicide Factors in Online Discourse: Sentiment and Thematic Analysis of Reddit
Dan, E., Zhu, J., & Jin, R. (2025) · ACM Transactions on the Web · doi:10.1145/3716546 · Cited by 4
Analysis of circadian rhythms from online communities of individuals with affective disorders
Dao, B., Nguyen, T., Venkatesh, S., & Phung, D. (2014) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2014.7058113 · Cited by 2
Generating Labeled Datasets of Twitter Users
Kiprov, Y., Gencheva, P., & Koychev, I. K. (2017) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3099023.3099048 · Cited by 2
Finding Behavioural and Imaging Biomarkers of Major Depressive Disorder (MDD) using Artificial Intelligence: A Review
Sheikh, S., & Shaban, K. B. (2020) · IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) · doi:10.1109/iciot48696.2020.9089576 · Cited by 2
Использование методики «Сетевой самоотчет» для изучения специфики интернет-социализации подростков и юношей с нарушением интеллекта
Kuzmina, T. I. (2021) · Российский психологический журнал · doi:10.21702/rpj.2021.1.1 · Cited by 2
An Approach to Detect Cyberbullying on Social Media
Ansari, F. S., Barhamgi, M., Khelifi, A., & Benslimane, D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-78428-7_5 · Cited by 2
Detection of Depression Signals from Social Media Data
Ahmad, T., Ahmed, S. U., & Ahmad, N. (2021) · Springer eBooks · doi:10.1007/978-3-030-76387-9_10 · Cited by 2
Identificando sintomas de depressão em postagens do Twitter em português do Brasil
Mendes, A. R., Passador, R. V. P., & Caseli, H. D. M. (2021) · Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL) · doi:10.5753/stil.2021.17795 · Cited by 2
Depression Identification Through Tweet Clusters
Masand, A., Chauhan, S., & Jain, T. K. (2022) · International Journal of Software Innovation · doi:10.4018/ijsi.297916 · Cited by 2
Identifying Chinese social media users' need for affect from their online behaviors
Deng, H., Zhao, N., & Wang, Y. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2022.1045279 · Cited by 2
A Scorecard Method for Detecting Depression in Social Media Users
Tefera, N. L., & Zhou, L. (2018) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2018.071 · Cited by 2
Characterizing negative sentiments in at-risk populations via crowd computing: a computational social science approach
García-Mancilla, J., Ramírez-Márquez, J. E., Lipizzi, C., Vesonder, G., & González, V. M. (2018) · International Journal of Data Science and Analytics · doi:10.1007/s41060-018-0135-9 · Cited by 2
Understanding Veterans Expression of Anger Using Social Media Analysis
Johnson, N., Coelho, J., Hossain, M. F., Kissane, T., Frydrychowi, W., Madiraju, P., Franco, Z. E., Hooyer, K., Annapureddy, P., & Ahamed, S. I. (2020) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac48688.2020.00-12 · Cited by 2
NLP-UNED at eRisk 2020: Self-harm Early Risk Detection with Sentiment Analysis and Linguistic Features.
Ageitos, E. C., Martínez-Romo, J., & Araujo, L. (2020) · CLEF (Working Notes) · Cited by 2
Affective Computing for Brain Health Disorders
Smith, E., Storch, E. A., Lavretsky, H., Cummings, J. L., & Eyre, H. A. (2021) · Springer eBooks · doi:10.1007/978-3-319-75479-6_36-1 · Cited by 2
Fusing Visual, Textual and Connectivity Clues for Studying Mental HealthPreprint
Yazdavar, A. H., Mahdavinejad, M. S., Bajaj, G., Romine, W. L., Monadjemi, A., Thirunarayan, K., Sheth, A., & Pathak, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.06843 · Cited by 2
Dumping the Closet Skeletons Online: Exploring the Guilty Information Disclosure Behavior on Social Media
Yang, Y., & Huang, Y. (2019) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v13i01.3267 · Cited by 1
The PEACE-Reviews dataset: Modeling Cognitive Appraisals in Emotion Text Analysis
Yeo, G., & Jaidka, K. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.186 · Cited by 2
Predicting the Transition From Depression to Suicidal Ideation Using Facebook Data Among Indian-Bangladeshi Individuals: Protocol for a Cohort Study
Turjo, M. D., Mundada, K. S., Haque, N. J., Ahmed, N., & Ahmed, N. (2024) · JMIR Research Protocols · doi:10.2196/55511 · Cited by 1
Identifying Marijuana Use Behaviors Among Youth Experiencing Homelessness Using a Machine Learning–Based Framework: Development and Evaluation Study
Deng, T., Urbaczewski, A., Lee, Y. J., Barman‐Adhikari, A., & Dewri, R. (2024) · JMIR AI · doi:10.2196/53488 · Cited by 1
Assessing the Effect of Writing Style Features in Detecting Depression on Social Media
Paz, L. H. B., Zareei, M., & Languré, A. D. L. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-93109-3_36 · Cited by 1
LLM Use for Mental Health: Crowdsourcing Users' Sentiment-based Perspectives and Values from Social Discussions
Li, L., Huang, X., Ma, R., Zhang, B. Z., Wu, H., Yang, F., & Chen, C. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3793025 · Cited by 4
Uncovering Temporal Trends of Desolation Among Social Media Users: A Data-Driven Analysis
Butt, A. U. R., Alzahrani, A., Khan, I., Qureshi, M. T., Khurshid, K., & Hameed, B. (2026) · Infosys science foundation series · doi:10.1007/978-981-95-2212-5_3 · Cited by 1
From Patient Emotion Recognition to Provider Understanding: A Multimodal Data Mining Framework for Emotion-Aware Clinical Counseling Systems
Mallarapu, S., Liu, X., Zargarian, P., Mottaghian, S., Suresha, R., Jain, V., & Bayat, A. (2026) · Computers · doi:10.3390/computers15030161 · Cited by 1
Immunizing the Social Network: Public Health and the “Troubled Teenager” in Digital Media
Banner, O. (2016) · Palgrave Macmillan UK eBooks · doi:10.1057/978-1-137-52141-5_5 · Cited by 1
YouTube as a Helpful and Dangerous Information Source for Deliberate Self-harming Behaviours
Alhassan, M. A., & Pennington, D. R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-96960-8_23 · Cited by 1
Twitter as a helping medium
Kabatnik, S. (2022) · Pragmatics & beyond. New series · doi:10.1075/pbns.331.13kab · Cited by 1
The Challenge of Early Risk Prediction on the Internet
Losada, D. E., Crestani, F., & Parapar, J. (2022) · Studies in computational intelligence · doi:10.1007/978-3-031-04431-1_2 · Cited by 1

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

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.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Social media as a measurement tool of depression in populations

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks