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Machine Learning-Based Approach for Depression Detection in Twitter Using Content and Activity Features
Hatoon S. ALSAGRI Mourad YKHLEF
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2020/08/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Data Engineering, Web Information Systems
social media analytics, depression detection, machine learning (ML), support vector machine (SVM), naive Bayes, decision tree, feature selection,
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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.