Depression Detection From Social Networks Data Based on Machine Learning and Deep Learning Techniques: An Interrogative Survey

Users can interact with one another through social networks (SNs) by exchanging information, delivering comments, finding new information, and engaging in discussions that result in the production of vast volumes of data daily. These data, available in various forms, such as images, text, and videos...

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Bibliographic Details
Published in:IEEE transactions on computational social systems 2023-08, Vol.10 (4), p.1-19
Main Authors: Hasib, Khan Md, Islam, Md Rafiqul, Sakib, Shadman, Akbar, Md. Ali, Razzak, Imran, Alam, Mohammad Shafiul
Format: Article
Language:English
Subjects:
Computer science
Deep learning
Deep learning (DL)
Depression
Diseases
Libraries
Literature reviews
Machine learning
machine learning (ML)
Mental depression
sentiment analysis
Social networking (online)
Social networks
social networks (SNs)
State-of-the-art reviews
Videos
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Summary:Users can interact with one another through social networks (SNs) by exchanging information, delivering comments, finding new information, and engaging in discussions that result in the production of vast volumes of data daily. These data, available in various forms, such as images, text, and videos, may be interpreted to reflect the user's activities, including their mental state regarding depression. For example, depression is a chronic disease from which the vast majority of users suffer, and it has emerged as a significant issue relating to mental health on a global scale. However, because these data are scant, unfinished, and sometimes given inaccurately, it is challenging to make an accurate automated diagnosis from them. Even though several procedures have been utilized over the past few decades to diagnose depression, machine learning (ML) and deep learning (DL) techniques supply superior insights. Thus, in this study, we review several state-of-the-art ML and DL techniques in terms of the systematic literature review (SLR) approach for depression detection. We also highlight some critical challenges from the existing literature that may help to explore for future study. Finally, we believe this survey will help readers and researchers in ML and DL to understand critical solutions in diagnosing depression.
ISSN:2329-924X
2373-7476
DOI:10.1109/TCSS.2023.3263128