Exploring the dominant features of social media for depression detection

Journal article


Hussain, J., Satti, F. A., Afzal, M., Khan, W., Bilal, H. S. M., Ansaar, M. Z., Ahmad, H. F. and Hur, T. 2019. Exploring the dominant features of social media for depression detection. Journal of Information Science. 46 (6). https://doi.org/10.1177/0165551519860469
AuthorsHussain, J., Satti, F. A., Afzal, M., Khan, W., Bilal, H. S. M., Ansaar, M. Z., Ahmad, H. F. and Hur, T.
Abstract

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.

Keywordssocial media; depression ; patient portal
Year2019
JournalJournal of Information Science
Journal citation46 (6)
PublisherSAGE Publications
ISSN1741-6485
Digital Object Identifier (DOI)https://doi.org/10.1177/0165551519860469
Web address (URL)https://journals.sagepub.com/doi/abs/10.1177/0165551519860469
Output statusPublished
Publication dates
Online12 Aug 2019
Publication process dates
Deposited26 Apr 2023
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https://repository.derby.ac.uk/item/9y363/exploring-the-dominant-features-of-social-media-for-depression-detection

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