# Machine Learning-based Approach for Depression Detection in Twitter Using Content and Activity Features

arXiv: https://arxiv.org/abs/2003.04763 · HTML: https://mlsanity.com/paper/2003.04763/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

## Built on by (most cited first, 9 of 9)

- [On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook](https://mlsanity.com/paper/2410.08793/) (2024)
- [Enhancing Mental Health Support through Human-AI Collaboration: Toward Secure and Empathetic AI-enabled chatbots](https://mlsanity.com/paper/2410.02783/) (2024)
- [ExBigBang: A Dynamic Approach for Explainable Persona Classification through Contextualized Hybrid Transformer Analysis](https://mlsanity.com/paper/2508.15364/) (2025)
- [Interpretability of Fine-grained Classification of Sadness and Depression](https://mlsanity.com/paper/2203.10432/) (2022)
- [Early Detection of Mental Health Issues Using Social Media Posts](https://mlsanity.com/paper/2503.07653/) (2025)
- [Factors That Influence the Adoption of AI-enabled Conversational Agents (AICAs) as an Augmenting Therapeutic Tool by Frontline Healthcare Workers: From Technology Acceptance Model 3 (TAM3) Lens -- A Systematic Mapping Review](https://mlsanity.com/paper/2504.13183/) (2025)
- [Classification of Psychiatry Clinical Notes by Diagnosis: A Deep Learning and Machine Learning Approach](https://mlsanity.com/paper/2508.00695/) (2025)
- [Explainable Detection of Depression Status Shifts from User Digital Traces](https://mlsanity.com/paper/2605.14995/) (2026)
- [FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data](https://mlsanity.com/paper/2605.18936/) (2026)

## Abstract (arXiv)

Social media channels, such as Facebook, Twitter, and Instagram, have altered our world forever. People are now increasingly connected than ever and reveal a sort of digital persona. Although social media certainly has several remarkable features, the demerits are undeniable as well. Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets. For this purpose, we trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from his/ her activities in the network and tweets. The results showed that the more features are used, the higher are the accuracy and F-measure scores in detecting depressed users. This method is a data-driven, predictive approach for early detection of depression or other mental illnesses. This study's main contribution is the exploration part of the features and its impact on detecting the depression level.