Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning Techniques
Depression is a widespread mental health disorder that may remain undiagnosed by conventional clinical methods. The rapidly growing world of social media sites such as Twitter, Reddit, Facebook, Instagram, and Weibo has provided new avenues for depression detection using Machine Learning (ML) as wel...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10843708/ |
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| author | Waleed Bin Tahir Shah Khalid Sulaiman Almutairi Mohammed Abohashrh Sufyan Ali Memon Jawad Khan |
| author_facet | Waleed Bin Tahir Shah Khalid Sulaiman Almutairi Mohammed Abohashrh Sufyan Ali Memon Jawad Khan |
| author_sort | Waleed Bin Tahir |
| collection | DOAJ |
| description | Depression is a widespread mental health disorder that may remain undiagnosed by conventional clinical methods. The rapidly growing world of social media sites such as Twitter, Reddit, Facebook, Instagram, and Weibo has provided new avenues for depression detection using Machine Learning (ML) as well as Deep Learning (DL), which analyze user behavior patterns and linguistic cues for more accurate detection of depression. Many techniques have been developed for this aim over the years. Identifying relevant publications on this topic using current academic search systems is challenging due to the rapid growth of research publications, unclear or limited search terms, and the complexity of citation networks. Several review papers have been published to ease this task by summarizing the methodologies, key findings, and recommendations for future research. However, most current reviews often do not provide a clear overview of the evolution, latest techniques, and challenges. This paper aims to address that gap by providing a comprehensive review of ML and DL methodologies for detecting depression on social media. We propose a generic architecture for these systems and present a detailed analysis of methodologies and datasets used for evaluation in this field. In addition, we highlight key open research areas, providing a useful starting point for further research and development. By narrowing our focus to social media, this review contributes to advancing the understanding and application of cutting-edge methods for depression detection. While this review highlights advancements in social media-based depression detection, it excludes alternative approaches like graph-based systems and reinforcement learning, and its focus on social media may limit its applicability to other domains. |
| format | Article |
| id | doaj-art-8d94586e76cc4fec9596471b705445ec |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8d94586e76cc4fec9596471b705445ec2025-01-25T00:02:21ZengIEEEIEEE Access2169-35362025-01-0113127891281810.1109/ACCESS.2025.353086210843708Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning TechniquesWaleed Bin Tahir0https://orcid.org/0009-0008-1312-8675Shah Khalid1https://orcid.org/0000-0001-5735-5863Sulaiman Almutairi2https://orcid.org/0000-0003-4810-6018Mohammed Abohashrh3Sufyan Ali Memon4https://orcid.org/0000-0001-5592-9990Jawad Khan5School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, PakistanSchool of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, PakistanDepartment of Health Informatics, College of Public Health and Health Informatics, Qassim University, Qassim, Saudi ArabiaDepartment of Basic Medical Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi ArabiaDepartment of Defense Systems Engineering, Sejong University, Seoul, Republic of KoreaSchool of Computing, Gachon University, Seongnam, South KoreaDepression is a widespread mental health disorder that may remain undiagnosed by conventional clinical methods. The rapidly growing world of social media sites such as Twitter, Reddit, Facebook, Instagram, and Weibo has provided new avenues for depression detection using Machine Learning (ML) as well as Deep Learning (DL), which analyze user behavior patterns and linguistic cues for more accurate detection of depression. Many techniques have been developed for this aim over the years. Identifying relevant publications on this topic using current academic search systems is challenging due to the rapid growth of research publications, unclear or limited search terms, and the complexity of citation networks. Several review papers have been published to ease this task by summarizing the methodologies, key findings, and recommendations for future research. However, most current reviews often do not provide a clear overview of the evolution, latest techniques, and challenges. This paper aims to address that gap by providing a comprehensive review of ML and DL methodologies for detecting depression on social media. We propose a generic architecture for these systems and present a detailed analysis of methodologies and datasets used for evaluation in this field. In addition, we highlight key open research areas, providing a useful starting point for further research and development. By narrowing our focus to social media, this review contributes to advancing the understanding and application of cutting-edge methods for depression detection. While this review highlights advancements in social media-based depression detection, it excludes alternative approaches like graph-based systems and reinforcement learning, and its focus on social media may limit its applicability to other domains.https://ieeexplore.ieee.org/document/10843708/Deep learningdepression detectionmachine learningnatural language processingsentiment analysissocial media |
| spellingShingle | Waleed Bin Tahir Shah Khalid Sulaiman Almutairi Mohammed Abohashrh Sufyan Ali Memon Jawad Khan Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning Techniques IEEE Access Deep learning depression detection machine learning natural language processing sentiment analysis social media |
| title | Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning Techniques |
| title_full | Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning Techniques |
| title_fullStr | Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning Techniques |
| title_full_unstemmed | Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning Techniques |
| title_short | Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning Techniques |
| title_sort | depression detection in social media a comprehensive review of machine learning and deep learning techniques |
| topic | Deep learning depression detection machine learning natural language processing sentiment analysis social media |
| url | https://ieeexplore.ieee.org/document/10843708/ |
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