Trends in Machine Learning Research on Children’s Mental Health and Online Gaming Disorder: A Bibliometric Perspective

Abstract

Mental health of children has become a great concern in this digital age. It is evident from multiple researches that increased use of internet and online gaming addiction enhances the mental problems in adolescents. This study presents a bibliometric analysis using Scopus database of 2,850 publications to explore research themes and trends by studying the network analysis of co-occurring keywords, highest citations of documents and authors in the domain of mental health, gaming disorders in adolescents and implementation of artificial intelligence applications. Further, bibliographic coupling of documents, authors, and organizations was conducted to know the insights of collaborations in research work. Key clusters highlighted research on mental health disorders, machine learning techniques, neurodevelopmental conditions, and digital wellbeing. The results suggested that United States hold the dominant position (documents: 1,122, citations: 61,865), and total link strength: 611). Among all the authors, David Shaffer has highest citations count and International Journal of Environmental Research and Public Health (IJERPH) have published highest count (114) of documents with 2891 citations, whereas Journal of Child Psychology and Psychiatry (JCPP) has got highest citations count (11,246) of 104 published documents. The findings suggest machine learning can be utilized to predict gaming disorders in children and deep learning models can be applied to assess sleep quality, particularly in children with gaming disorders and mental health conditions.