mlsanityData to 7 Oct 2026

Datasets for Depression Modeling in Social Media: An Overview

arXiv 2503.21513 ↗First posted 27 Mar 2025cs.CLcited by 5

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Abstract (arXiv)

Depression is the most common mental health disorder, and its prevalence increased during the COVID-19 pandemic. As one of the most extensively researched psychological conditions, recent research has increasingly focused on leveraging social media data to enhance traditional methods of depression screening. This paper addresses the growing interest in interdisciplinary research on depression, and aims to support early-career researchers by providing a comprehensive and up-to-date list of datasets for analyzing and predicting depression through social media data. We present an overview of datasets published between 2019 and 2024. We also make the comprehensive list of datasets available online as a continuously updated resource, with the hope that it will facilitate further interdisciplinary research into the linguistic expressions of depression on social media.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 88 references that are in the map. First posted on arXiv 27 Mar 2025.

Builds on

21 of 88 references.

References · 21

  1. 2017Depression and Self-Harm Risk Assessment in Online Forumscited by 81
  2. 2021Deep Learning for Suicide and Depression Identification with Unsupervised Label Correctioncited by 45
  3. 2020On the State of Social Media Data for Mental Health Researchcited by 32
  4. 2022Data set creation and empirical analysis for detecting signs of depression from social media postingscited by 26
  5. 2022Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnairescited by 26
  6. 2017Multi-Task Learning for Mental Health using Social Media Textcited by 25
  7. 2020Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Frameworkcited by 25
  8. 2022Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Postscited by 23
  9. 2022Symptom Identification for Interpretable Detection of Multiple Mental Disorderscited by 20
  10. 2019What Twitter Profile and Posted Images Reveal About Depression and Anxietycited by 17
  11. 2021An Exploratory Analysis of the Relation Between Offensive Language and Mental Healthcited by 14
  12. 2020Towards Preemptive Detection of Depression and Anxiety in Twittercited by 13
9 more
  1. 2022DEPTWEET: A Typology for Social Media Texts to Detect Depression Severitiescited by 12
  2. 2024Diverse Perspectives, Divergent Models: Cross-Cultural Evaluation of Depression Detection on Twittercited by 12
  3. 2020Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australiacited by 10
  4. 2020Temporal Mental Health Dynamics on Social Mediacited by 6
  5. 2024On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlookcited by 6
  6. 2023DepreSym: A Depression Symptom Annotated Corpus and the Role of LLMs as Assessors of Psychological Markerscited by 5
  7. 2022Emotion-based Modeling of Mental Disorders on Social Mediacited by 4
  8. 2023A Simple and Flexible Modeling for Mental Disorder Detection by Learning from Clinical Questionnairescited by 4
  9. 2024Your Model Is Not Predicting Depression Well And That Is Why: A Case Study of PRIMATE Datasetcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025A Survey on Multilingual Mental Disorders Detection from Social Media Datacited by 3
  2. 2025Unveiling the Landscape of Clinical Depression Assessment: From Behavioral Signatures to Psychiatric Reasoningcited by 2
  3. 2025CNSocialDepress: A Chinese Social Media Dataset for Depression Risk Detection and Structured Analysiscited by 1
  4. 2026Mental Health Disorder Detection Beyond Social Media: A Systematic Review of Available Datasetscited by 0
  5. 2026WPG-MoE: Weak-Prior-Guided Dense Mixture-of-Experts for User-Level Social Media Depression Detectioncited by 0