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

Artificial Intelligence (1198) Machine Learning (1385) Natural Language Processing (644) Peer-to-Peer Support and Online Communities (597) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1570) Methods and New Tools in Mental Health Research (424)

Published on in Vol 23, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24870, first published .
Machine Learning for Mental Health in Social Media: Bibliometric Study

Machine Learning for Mental Health in Social Media: Bibliometric Study

Machine Learning for Mental Health in Social Media: Bibliometric Study

Authors of this article:

Jina Kim1 Author Orcid Image ;   Daeun Lee1 Author Orcid Image ;   Eunil Park1 Author Orcid Image
Article Authors Cited by (48) Tweetations (19) Metrics

Journals

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  2. Resnik P, De Choudhury M, Musacchio Schafer K, Coppersmith G. Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on “Machine Learning for Mental Health in Social Media: Bibliometric Study”. Journal of Medical Internet Research 2021;23(6):e28990 View
  3. Kim J, Lee D, Park E. Authors’ Reply to: Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on “Machine Learning for Mental Health in Social Media: Bibliometric Study”. Journal of Medical Internet Research 2021;23(6):e29549 View
  4. Amanat A, Rizwan M, Javed A, Abdelhaq M, Alsaqour R, Pandya S, Uddin M. Deep Learning for Depression Detection from Textual Data. Electronics 2022;11(5):676 View
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  6. Yang K, Qi H. Research on Health Disparities Related to the COVID-19 Pandemic: A Bibliometric Analysis. International Journal of Environmental Research and Public Health 2022;19(3):1220 View
  7. Sami Khafaga D, Auvdaiappan M, Deepa K, Abouhawwash M, Khalid Karim F. Deep Learning for Depression Detection Using Twitter Data. Intelligent Automation & Soft Computing 2023;36(2):1301 View
  8. Skaik R, Inkpen D. Predicting Depression in Canada by Automatic Filling of Beck’s Depression Inventory Questionnaire. IEEE Access 2022;10:102033 View
  9. Yang K, Qi H. The Public Health Governance of the COVID-19 Pandemic: A Bibliometric Analysis. Healthcare 2022;10(2):299 View
  10. Yadav U, Sharma A. A novel automated depression detection technique using text transcript. International Journal of Imaging Systems and Technology 2023;33(1):108 View
  11. Zhang Y, Liu X, Qiao X, Fan Y. Characteristics and Emerging Trends in Research on Rehabilitation Robots from 2001 to 2020: Bibliometric Study. Journal of Medical Internet Research 2023;25:e42901 View
  12. Yogeswaran V, Morr C. Mental Health for Medical Students, what do we know today?. Procedia Computer Science 2022;198:307 View
  13. Sarabadani S, Baruah G, Fossat Y, Jeon J. Longitudinal Changes of COVID-19 Symptoms in Social Media: Observational Study. Journal of Medical Internet Research 2022;24(2):e33959 View
  14. Di Cara N, Maggio V, Davis O, Haworth C. Methodologies for Monitoring Mental Health on Twitter: Systematic Review. Journal of Medical Internet Research 2023;25:e42734 View
  15. 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
  16. Shi J, Bendig D, Vollmar H, Rasche P. Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study. Journal of Medical Internet Research 2023;25:e45815 View
  17. Ezugwu A, Greeff J, Ho Y. A comprehensive study of groundbreaking machine learning research: Analyzing highly cited and impactful publications across six decades. Journal of Engineering Research 2023 View
  18. Khorasani M, Kahani M, Yazdi S, Hajiaghaei-Keshteli M. Towards finding the lost generation of autistic adults: A deep and multi-view learning approach on social media. Knowledge-Based Systems 2023;276:110724 View
  19. Ahmed E, Xue L, Sankalp A, Kong H, Matos A, Silenzio V, Singh V. Predicting Loneliness through Digital Footprints on Google and YouTube. Electronics 2023;12(23):4821 View
  20. Xin R, Lim Y. Bibliometric analysis of literature on social media trends during the COVID-19 pandemic. Online Information Review 2024;48(4):764 View
  21. Arora M, Singh J, Singh A. Development of intelligent system based on synthesis of affective signals and deep neural networks to foster mental health of the Indian virtual community. Social Network Analysis and Mining 2024;14(1) View
  22. Sandu A, Ioanăș I, Delcea C, Florescu M, Cotfas L. Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research. Algorithms 2024;17(2):70 View
  23. Rani S, Ahmed K, Subramani S. From Posts to Knowledge: Annotating a Pandemic-Era Reddit Dataset to Navigate Mental Health Narratives. Applied Sciences 2024;14(4):1547 View
  24. Kaur I, Kamini , Kaur J, Gagandeep , Singh S, Gupta U. Enhancing explainability in predicting mental health disorders using human–machine interaction. Multimedia Tools and Applications 2024 View
  25. 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
  26. Sandu A, Cotfas L, Stănescu A, Delcea C. A Bibliometric Analysis of Text Mining: Exploring the Use of Natural Language Processing in Social Media Research. Applied Sciences 2024;14(8):3144 View
  27. Damar M, Küme T, Yüksel İ, Çetinkol A, K. Pal J, Safa Erenay F. Medical Informatics as a Concept and Field-Based Medical Informatics Research: The Case of Turkey. Düzce Tıp Fakültesi Dergisi 2024;26(1):44 View
  28. Maćkowska S, Koścień B, Wójcik M, Rojewska K, Spinczyk D. Using Natural Language Processing for a Computer-Aided Rapid Assessment of the Human Condition in Terms of Anorexia Nervosa. Applied Sciences 2024;14(8):3367 View
  29. Ali A, Nazim M, Qaiser S, Malik R. Does Research Funding, Open Access Availability, and Collaboration in Research Influence Citation Impact? An Analysis of Neurotechnology Research. Journal of Electronic Resources in Medical Libraries 2024;21(2):53 View
  30. Jang J, Ahn H, Park E. Analysis of a Collaborative Research Network of Botulinum Toxin Clinical Trials. Sage Open 2024;14(2) View

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