Work

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

Year: 2020

Type: article

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.

Source: IEICE Transactions on Information and Systems

DOI: 10.1587/transinf.2020edp7023

Authors: Hatoon S. ALSAGRI, Mourad YKHLEF

Institutions: Imam Mohammad ibn Saud Islamic University, King Saud University

Language: English

Topic: Mental Health via Writing

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: Ain Shams University, King Saud University, Deanship of Scientific Research, King Saud University