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

Letters (85) Artificial Intelligence (1371) Machine Learning (1465) Natural Language Processing (692) Peer-to-Peer Support and Online Communities (636) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Methods and New Tools in Mental Health Research (454)

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

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28990, first published .
Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on “Machine Learning for Mental Health in Social Media: Bibliometric Study”

Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on “Machine Learning for Mental Health in Social Media: Bibliometric Study”

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:

Philip Resnik1 Author Orcid Image ;   Munmun De Choudhury2 Author Orcid Image ;   Katherine Musacchio Schafer3 Author Orcid Image ;   Glen Coppersmith4 Author Orcid Image
Article Authors Cited by (2) Tweetations (3) Metrics

Philip Resnik   1 , PhD ;   Munmun De Choudhury   2 , PhD ;   Katherine Musacchio Schafer   3 , MEd, MS ;   Glen Coppersmith   4 , PhD

1 Department of Linguistics and Institute for Advanced Computer Studies, University of Maryland, College Park, MD, United States

2 School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States

3 Department of Psychology, Florida State University, Tallahassee, FL, United States

4 Qntfy, Boston, MA, United States

Corresponding Author:

  • Philip Resnik, PhD
  • Department of Linguistics and Institute for Advanced Computer Studies
  • University of Maryland
  • 1401 Marie Mount Hall
  • College Park, MD, 20814
  • United States
  • Phone: 1 301 405 7002
  • Email: resnik@umd.edu