2020/03/09 by Hatoon S. AlSagri, Mourad Ykhlef
Computer Science · Mathematics · Psychology · #Artificial intelligence #Computer science #Computer security #Correlation #Depression (economics) #Digital Mental Health Interventions #Exploit #Human–computer interaction #Information retrieval #Internet privacy #Machine learning #Mental Health via Writing #Persona #Sentiment Analysis and Opinion Mining #Social media #World Wide Web #cs.LG #cs.SI #sort #stat.ML
paper · pdf · doi:10.1587/transinf.2020edp7023
16 pages, 7 figures, journal article
arxiv created 2020/03/09 · openalex publication_date 2020/07/31 · arxiv updated 2020/08/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.