Exploring the dominant features of social media for depression detection

• Published in: Journal of information science Vol. 46; no. 6; pp. 739 - 759
• Main Authors: Hussain, Jamil, Satti, Fahad Ahmed, Afzal, Muhammad, Khan, Wajahat Ali, Bilal, Hafiz Syed Muhammad, Ansaar, Muhammad Zaki, Ahmad, Hafiz Farooq, Hur, Taeho, Bang, Jaehun, Kim, Jee-In, Park, Gwang Hoon, Seung, Hyonwoo, Lee, Sungyoung
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-12-2020; Bowker-Saur Ltd
• Subjects: Digital media; Machine learning; Mental depression; Physicians; Signs and symptoms; Social networks; Web portals; Depression; value-added information; mental illness; Facebook
• ISSN: 0165-5515; 1741-6485
• DOI: 10.1177/0165551519860469

Recently, social media have been used by researchers to detect depressive symptoms in individuals using linguistic data from users’ posts. In this study, we propose a framework to identify social information as a significant predictor of depression. Using the proposed framework, we develop an application called the Socially Mediated Patient Portal (SMPP), which detects depression-related markers in Facebook users by applying a data-driven approach with machine learning classification techniques. We examined a data set of 4350 users who were evaluated for depression using the Center for Epidemiological Studies Depression (CES-D) scale. From this analysis, we identified a set of features that can distinguish between individuals with and without depression. Finally, we identified the dominant features that adequately assess individuals with and without depression on social media. The model trained on these features will be helpful to physicians in diagnosing mental diseases and psychiatrists in analysing patient behaviour.