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

**Authors:** Hatoon S. AlSagri, Mourad Ykhlef  
**arXiv:** [2003.04763](https://arxiv.org/abs/2003.04763)  
**Date:** 2020-03-09  
**Summary page:** https://www.summarizepaper.com/fr/paper/2003.04763v1/

## Abstract

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.