Vasileios Stavropoulos, Maria Prokofieva, Daniel Zarate, Michelle Colder Carras, Rabindra Ratan, Rachel Kowert, Bruno Schivinski, Tyrone L Burleigh, Dylan Poulus, Leila Karimi, Angela Gorman-Alesi, Taylor Brown, Rapson Gomez, Kaiden Hein, Nalin Arachchilage, Mark D Griffiths
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Machine Learning(s) in gaming disorder through the user-avatar bond: A step towards conceptual and methodological clarity.
In response to our study, the commentary by Infanti et al. (2024) raised critical points regarding (i) the conceptualization and utility of the user-avatar bond in addressing gaming disorder (GD) risk, and (ii) the optimization of supervised machine learning techniques applied to assess GD risk. To advance the scientific dialogue and progress in these areas, the present paper aims to: (i) enhance the clarity and understanding of the concepts of the avatar, the user-avatar bond, and the digital phenotype concerning gaming disorder (GD) within the broader field of behavioral addictions, and (ii) comparatively assess how the user-avatar bond (UAB) may predict GD risk, by both removing data augmentation before the data split and by implementing alternative data imbalance treatment approaches in programming.
期刊介绍：
The aim of Journal of Behavioral Addictions is to create a forum for the scientific information exchange with regard to behavioral addictions. The journal is a broad focused interdisciplinary one that publishes manuscripts on different approaches of non-substance addictions, research reports focusing on the addictive patterns of various behaviors, especially disorders of the impulsive-compulsive spectrum, and also publishes reviews in these topics. Coverage ranges from genetic and neurobiological research through psychological and clinical psychiatric approaches to epidemiological, sociological and anthropological aspects.
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