  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.
