# Identifying Depression on Twitter

arXiv: https://arxiv.org/abs/1607.07384 · HTML: https://mlsanity.com/paper/1607.07384/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2016.

## Built on by (most cited first, 10 of 10)

- [Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction](https://mlsanity.com/paper/2102.09427/) (2021)
- [DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media](https://mlsanity.com/paper/2105.10878/) (2021)
- [A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19](https://mlsanity.com/paper/2411.07163/) (2024)
- [Depression Detection Using Digital Traces on Social Media: A Knowledge-aware Deep Learning Approach](https://mlsanity.com/paper/2303.05389/) (2023)
- [Enhancing Depression Detection via Question-wise Modality Fusion](https://mlsanity.com/paper/2503.20496/) (2025)
- [Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study](https://mlsanity.com/paper/2402.05571/) (2024)
- [Mining Mental Health Signals: A Comparative Study of Four Machine Learning Methods for Depression Detection from Social Media Posts in Sorani Kurdish](https://mlsanity.com/paper/2508.15829/) (2025)
- [Social Media for Mental Health: Data, Methods, and Findings](https://mlsanity.com/paper/2511.07914/) (2025)
- [RSPC: A Benchmark for Modeling Stress and Psychiatric Conditions in Digitally Mediated Relationships using Psychiatrist Annotations](https://mlsanity.com/paper/2606.27247/) (2026)
- [Analysing Self-Harm Representations in Language Models: a Cross-Architecture Study](https://mlsanity.com/paper/2607.21988/) (2026)

## Abstract (arXiv)

Social media has recently emerged as a premier method to disseminate information online. Through these online networks, tens of millions of individuals communicate their thoughts, personal experiences, and social ideals. We therefore explore the potential of social media to predict, even prior to onset, Major Depressive Disorder (MDD) in online personas. We employ a crowdsourced method to compile a list of Twitter users who profess to being diagnosed with depression. Using up to a year of prior social media postings, we utilize a Bag of Words approach to quantify each tweet. Lastly, we leverage several statistical classifiers to provide estimates to the risk of depression. Our work posits a new methodology for constructing our classifier by treating social as a text-classification problem, rather than a behavioral one on social media platforms. By using a corpus of 2.5M tweets, we achieved an 81% accuracy rate in classification, with a precision score of .86. We believe that this method may be helpful in developing tools that estimate the risk of an individual being depressed, can be employed by physicians, concerned individuals, and healthcare agencies to aid in diagnosis, even possibly enabling those suffering from depression to be more proactive about recovering from their mental health.