# Data set creation and empirical analysis for detecting signs of depression from social media postings

arXiv: https://arxiv.org/abs/2202.03047 · HTML: https://mlsanity.com/paper/2202.03047/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 31 references that are in the map.
- First posted on arXiv 7 Feb 2022.

## Builds on (most cited first, 2 of 2)

- [Instagram photos reveal predictive markers of depression](https://mlsanity.com/paper/1608.03282/) (2016)
- [Forecasting the onset and course of mental illness with Twitter data](https://mlsanity.com/paper/1608.07740/) (2016)

## Built on by (most cited first, 26 of 26)

- [A Survey of Large Language Models in Medicine: Progress, Application, and Challenge](https://mlsanity.com/paper/2311.05112/) (2023)
- [Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data](https://mlsanity.com/paper/2307.14385/) (2023)
- [Emotion fusion for mental illness detection from social media: A survey](https://mlsanity.com/paper/2304.09493/) (2023)
- [A Comprehensive Evaluation of Large Language Models on Mental Illnesses](https://mlsanity.com/paper/2409.15687/) (2024)
- [On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook](https://mlsanity.com/paper/2410.08793/) (2024)
- [Datasets for Depression Modeling in Social Media: An Overview](https://mlsanity.com/paper/2503.21513/) (2025)
- [A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context](https://mlsanity.com/paper/2501.06859/) (2025)
- [DepressionX: Knowledge Infused Residual Attention for Explainable Depression Severity Assessment](https://mlsanity.com/paper/2501.14985/) (2025)
- [A Survey on Multilingual Mental Disorders Detection from Social Media Data](https://mlsanity.com/paper/2505.15556/) (2025)
- [Sentiment Informed Sentence BERT-Ensemble Algorithm for Depression Detection](https://mlsanity.com/paper/2409.13713/) (2024)
- [On-device Federated Learning in Smartphones for Detecting Depression from Reddit Posts](https://mlsanity.com/paper/2410.13709/) (2024)
- [Leveraging LLMs for Mental Health: Detection and Recommendations from Social Discussions](https://mlsanity.com/paper/2503.01442/) (2025)
- [Mental-R1: Aligning LLM Reasoning for Mental Health Assessment](https://mlsanity.com/paper/2606.13176/) (2026)
- [KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text](https://mlsanity.com/paper/2204.04481/) (2022)
- [Detect Depression from Social Networks with Sentiment Knowledge Sharing](https://mlsanity.com/paper/2306.14903/) (2023)
- [DeepLearningBrasil@LT-EDI-2023: Exploring Deep Learning Techniques for Detecting Depression in Social Media Text](https://mlsanity.com/paper/2311.05047/) (2023)
- [Decoding Linguistic Nuances in Mental Health Text Classification Using Expressive Narrative Stories](https://mlsanity.com/paper/2412.16302/) (2024)
- [XAI in Computational Linguistics: Understanding Political Leanings in the Slovenian Parliament](https://mlsanity.com/paper/2305.04631/) (2023)
- [Detection of depression on social networks using transformers and ensembles](https://mlsanity.com/paper/2305.05325/) (2023)
- [A Cost-aware Study of Depression Language on Social Media using Topic and Affect Contextualization](https://mlsanity.com/paper/2306.17564/) (2023)
- [Vulgar Remarks Detection in Chittagonian Dialect of Bangla](https://mlsanity.com/paper/2308.15448/) (2023)
- [Digital Epidemiology: Leveraging Social Media for Insight into Epilepsy and Mental Health](https://mlsanity.com/paper/2412.01692/) (2024)
- [MHINDR -- a DSM5 based mental health diagnosis and recommendation framework using LLM](https://mlsanity.com/paper/2509.25992/) (2025)
- [AttentionDep: Domain-Aware Attention for Explainable Depression Severity Assessment](https://mlsanity.com/paper/2510.00706/) (2025)
- [Language-Independent Sentiment Labelling with Distant Supervision: A Case Study for English, Sepedi and Setswana](https://mlsanity.com/paper/2511.19818/) (2025)
- [Structure vs. Chain-of-Thought: Evaluating LLM Criteria Extraction for Depression Severity](https://mlsanity.com/paper/2609.39049/) (2026)

## Abstract (arXiv)

Depression is a common mental illness that has to be detected and treated at an early stage to avoid serious consequences. There are many methods and modalities for detecting depression that involves physical examination of the individual. However, diagnosing mental health using their social media data is more effective as it avoids such physical examinations. Also, people express their emotions well in social media, it is desirable to diagnose their mental health using social media data. Though there are many existing systems that detects mental illness of a person by analysing their social media data, detecting the level of depression is also important for further treatment. Thus, in this research, we developed a gold standard data set that detects the levels of depression as `not depressed', `moderately depressed' and `severely depressed' from the social media postings. Traditional learning algorithms were employed on this data set and an empirical analysis was presented in this paper. Data augmentation technique was applied to overcome the data imbalance. Among the several variations that are implemented, the model with Word2Vec vectorizer and Random Forest classifier on augmented data outperforms the other variations with a score of 0.877 for both accuracy and F1 measure.