Abstract
BackgroundSocial media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention.ObjectiveWe aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media.MethodsPublications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described.ResultsWe obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with Lecture Notes in Computer Science and Journal of Medical Internet Research as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated.ConclusionsThe results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners.
Highlights
BackgroundArtificial intelligence (AI) has permeated various daily sectors that are directly related to our lives [1,2]
Several scholars have revealed that individuals who suffer from mental disorders tend to prefer sharing their personal information and seeking assistance to reduce their concerns through online channels rather than with medical providers such as counselors or therapists [9,10,11]
We examined the trends of research using machine learning (ML) for mental health in social media by employing (1) a bibliometric analysis to determine the publication distributions on sources, authors, institutions, countries, research subjects, and author keywords; and (2) a trend review analysis to determine the distributions of citation numbers, along with a comprehensive review of highly cited publications
Summary
Artificial intelligence (AI) has permeated various daily sectors that are directly related to our lives [1,2] With this trend, AI for health, which refers to applying AI to real-world health care, has become one of the most important social issues at present [3,4]. Extensive efforts have been put forward to employ AI technologies in health care services in addressing issues related to physical health, involving several medical centers, researchers, and organizations, as well as for mental health as a rapidly growing social issues. The World Health Organization estimates that approximately 1 billion people worldwide have mental disorders [7]. Social media platforms provide an accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention
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