This paper is in the following e-collection/theme issue:

Reviews in Digital Mental Health (225) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1553) e-Mental Health and Cyberpsychology (1388) Peer-to-Peer Support and Online Communities (594) Depression and Mood Disorders; Suicide Prevention (1273) Machine Learning (1373) Artificial Intelligence (1189) Mass Media/Social Media Communication and Campaigns (446)

Published on in Vol 9, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27244, first published .
Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review

Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review

Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review

Authors of this article:

Danxia Liu1 Author Orcid Image ;   Xing Lin Feng2 Author Orcid Image ;   Farooq Ahmed3, 4 Author Orcid Image ;   Muhammad Shahid5 Author Orcid Image ;   Jing Guo2 Author Orcid Image
Article Authors Cited by (30) Tweetations (4) Metrics

Journals

  1. Baghdadi N, Malki A, Magdy Balaha H, AbdulAzeem Y, Badawy M, Elhosseini M. An optimized deep learning approach for suicide detection through Arabic tweets. PeerJ Computer Science 2022;8:e1070 View
  2. Kesler S, Henneghan A, Thurman W, Rao V. Identifying Themes for Assessing Cancer-Related Cognitive Impairment: Topic Modeling and Qualitative Content Analysis of Public Online Comments. JMIR Cancer 2022;8(2):e34828 View
  3. Pan W, Wang X, Zhou W, Hang B, Guo L. Linguistic Analysis for Identifying Depression and Subsequent Suicidal Ideation on Weibo: Machine Learning Approaches. International Journal of Environmental Research and Public Health 2023;20(3):2688 View
  4. Bhattacharya M, Roy S, Chattopadhyay S, Das A, Shetty S. A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges. SECURITY AND PRIVACY 2023;6(1) View
  5. Barua P, Vicnesh J, Lih O, Palmer E, Yamakawa T, Kobayashi M, Acharya U. Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review. Cognitive Neurodynamics 2024;18(1):1 View
  6. Malhotra A, Jindal R. Deep learning techniques for suicide and depression detection from online social media: A scoping review. Applied Soft Computing 2022;130:109713 View
  7. Lyu S, Ren X, Du Y, Zhao N. Detecting depression of Chinese microblog users via text analysis: Combining Linguistic Inquiry Word Count (LIWC) with culture and suicide related lexicons. Frontiers in Psychiatry 2023;14 View
  8. Yao S, Wang F, Chen J, Lu Q. Utilizing health-related text on social media for depression research: themes and methods. Library Hi Tech 2023 View
  9. Akyol S. New chaos-integrated improved grey wolf optimization based models for automatic detection of depression in online social media and networks. PeerJ Computer Science 2023;9:e1661 View
  10. Chen J, Hu Y, Lai Q, Wang W, Chen J, Liu H, Srivastava G, Bashir A, Hu X. IIFDD: Intra and inter-modal fusion for depression detection with multi-modal information from Internet of Medical Things. Information Fusion 2024;102:102017 View
  11. Thushari P, Aggarwal N, Vajrobol V, Saxena G, Singh S, Pundir A. Identifying discernible indications of psychological well-being using ML: explainable AI in reddit social media interactions. Social Network Analysis and Mining 2023;13(1) View
  12. Mao K, Wu Y, Chen J. A systematic review on automated clinical depression diagnosis. npj Mental Health Research 2023;2(1) View
  13. Counts N, Bloom D, Halfon N. Psychological distress as a systemic economic risk in the USA. Nature Mental Health 2023;1(12):950 View
  14. Liu Y. Depression detection via a Chinese social media platform: a novel causal relation-aware deep learning approach. The Journal of Supercomputing 2024;80(8):10327 View
  15. Tudehope L, Harris N, Vorage L, Sofija E. What methods are used to examine representation of mental ill-health on social media? A systematic review. BMC Psychology 2024;12(1) View
  16. Khan A, Ali R. Unraveling minds in the digital era: a review on mapping mental health disorders through machine learning techniques using online social media. Social Network Analysis and Mining 2024;14(1) View
  17. Gan L, Guo Y, Yang T. Machine Learning for Depression Detection on Web and Social Media. International Journal on Semantic Web and Information Systems 2024;20(1):1 View
  18. Yuan Y, Kasson E, Taylor J, Cavazos-Rehg P, De Choudhury M, Aledavood T. Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media: Machine Learning Approach. JMIR Formative Research 2024;8:e54433 View
  19. Prof. Saba Anjum Patel , Kalakshi Jadhav , Sayali Ligade , Vishal Mahajan , Keshav Anant . Depression Prediction using Machine Learning Algorithms. International Journal of Advanced Research in Science, Communication and Technology 2024:526 View
  20. Abbasi A, Parsons J, Pant G, Sheng O, Sarker S. Pathways for Design Research on Artificial Intelligence. Information Systems Research 2024;35(2):441 View
  21. Pandey M, Litoriya R, Pandey P, Panigrahi P, Gupta B. Trapezoidal fuzzy number methodology for prioritising the predictors of social media addiction. Enterprise Information Systems 2024 View
  22. Park Y, Park S, Lee M. Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review. Journal of Affective Disorders 2024;361:445 View
  23. Wang L, Zhang Y, Zhou B, Cao S, Hu K, Tan Y. Automatic depression prediction via cross-modal attention-based multi-modal fusion in social networks. Computers and Electrical Engineering 2024;118:109413 View

Books/Policy Documents

  1. Jickson S, Anoop V, Asharaf S. Proceedings of International Conference on Information Technology and Applications. View
  2. Kansal M, Singh P, Srivastava P, Singhal R, Deep N, Singh A. Future of AI in Medical Imaging. View