Stavropoulos, Vasileios ORCID: 0000-0001-6964-4662, Prokofieva, Maria ORCID: 0000-0003-1974-3827, Zarate, Daniel ORCID: 0000-0002-1508-8637, Colder Carras, Michelle ORCID: 0000-0003-0750-524X, Ratan, Rabindra ORCID: 0000-0001-7611-8046, Kowert, Rachel ORCID: 0000-0002-9522-9945, Schivinski, Bruno ORCID: 0000-0002-4095-1922, Burleigh, Tyrone L ORCID: 0000-0002-3405-140X, Poulus, Dylan R ORCID: 0000-0003-4502-6821, Karimi, Leila ORCID: 0000-0003-2364-504X, Gorman-Alesi, Angela, Brown, Taylor ORCID: 0000-0001-9814-3832, Gomez, Rapson ORCID: 0000-0001-7637-1551, Hein, Kaiden ORCID: 0000-0002-1758-9208, Arachchilage, Nalin ORCID: 0000-0002-0059-0376 and Griffiths, Mark D ORCID: 0000-0001-8880-6524 (2024) Machine Learning(s) in gaming disorder through the user-avatar bond: A step towards conceptual and methodological clarity Reply to: User-avatar bond as diagnostic indicator for gaming disorder: A word on the side of caution (2024). Journal of Behavioral Addictions, 13 (4). pp. 894-900. ISSN 2062-5871

Abstract

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.

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