Machine Learning(s) in gaming disorder through the user-avatar bond: A step towards conceptual and methodological clarity
Year: 2025
Type: article
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.
Source: Figshare
Authors: Vasileios Stavropoulos (18039982), Maria Prokofieva (13921392), Daniel Zarate (18040459), Michelle Colder Carras (20089719), Rabindra Ratan (7511905), Rachel Kowert (19278979), Bruno Schivinski (18032971), Tyrone L Burleigh (13593250), Dylan Poulus (18392158), Leila Karimi (18037039), Angela Gorman-Alesi (20883770), Taylor Brown (18040417), Rapson Gomez (18306547), Kaiden Hein (18511322), Nalin Asanka Gamagedara Arachchilage (18043414), Mark D Griffiths (3697978)
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7075340795
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