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

Letters (85) Artificial Intelligence (1393) Machine Learning (1480) Natural Language Processing (696) Peer-to-Peer Support and Online Communities (637) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1645) Methods and New Tools in Mental Health Research (457)

Published on in Vol 23, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29549, first published .
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”

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”

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”

Authors of this article:

Jina Kim1 Author Orcid Image ;   Daeun Lee1 Author Orcid Image ;   Eunil Park1 Author Orcid Image
Article Authors Cited by Tweetations (1) Metrics

Jina Kim   1 , BSc, MSc ;   Daeun Lee   1 , BA ;   Eunil Park   1 , BSc, MSc, PhD

1 Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea

Corresponding Author:

  • Eunil Park, BSc, MSc, PhD
  • Department of Applied Artificial Intelligence
  • Sungkyunkwan University
  • 312 International Hall
  • Sungkyunkwan-ro 25-2
  • Seoul, 03063
  • Republic of Korea
  • Phone: 82 2 740 1864
  • Fax: 82 2 740 1856
  • Email: eunilpark@skku.edu