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”
Author(s) -
Jina Kim,
Daeun Lee,
Eunil Park
Publication year - 2021
Publication title -
journal of medical internet research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.446
H-Index - 142
eISSN - 1439-4456
pISSN - 1438-8871
DOI - 10.2196/29549
Subject(s) - mental health , social media , psychology , bibliometrics , applied psychology , psychiatry , computer science , library science , world wide web
First, Resnik et al [2] pointed out the limited scope of the search query selected in our bibliometric study, which concealed the approaches taken in the clinical research field. We agree that the search query may seem to be lacking in covering clinical research; however, we did not intend to distort the recent trends in machine learning for mental health in social media. Moreover, considering that publication venues including the Journal of Medical Internet Research, BMJ Open, International Journal of Environmental Research and Public Health, Frontiers in Psychiatry, and Frontiers in Psychology were listed as productive publication sources in the analysis results, we believe that the publications employed in our analysis cover the invaluable research methodologies in the medical research area. The completed list of retrieved publications can be found in the appendix [1].
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