Deep learning(s) in gaming disorder through the user-avatar bond: A longitudinal study using machine learning.

Autor: Stavropoulos V; 1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia.; 3National and Kapodistrian University of Athens, Greece., Zarate D; 1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia., Prokofieva M; 2Victoria University, Australia., Van de Berg N; 4The Three Seas Psychology, Australia., Karimi L; 1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia., Gorman Alesi A; 5Catholic Care Victoria, Australia., Richards M; 6Mighty Serious, Australia., Bennet S; 7Quantum Victoria, Australia., Griffiths MD; 8International Gaming Research Unit, Psychology Department, Nottingham Trent University, UK.
Jazyk: angličtina
Zdroj: Journal of behavioral addictions [J Behav Addict] 2023 Nov 09; Vol. 12 (4), pp. 878-894. Date of Electronic Publication: 2023 Nov 09 (Print Publication: 2023).
DOI: 10.1556/2006.2023.00062
Abstrakt: Background and Aims: Gaming disorder [GD] risk has been associated with the way gamers bond with their visual representation (i.e., avatar) in the game-world. More specifically, a gamer's relationship with their avatar has been shown to provide reliable mental health information about the user in their offline life, such as their current and prospective GD risk, if appropriately decoded.
Methods: To contribute to the paucity of knowledge in this area, 565 gamers (Mage = 29.3 years; SD =10.6) were assessed twice, six months apart, using the User-Avatar-Bond Scale (UABS) and the Gaming Disorder Test. A series of tuned and untuned artificial intelligence [AI] classifiers analysed concurrently and prospectively their responses.
Results: Findings showed that AI models learned to accurately and automatically identify GD risk cases, based on gamers' reported UABS score, age, and length of gaming involvement, both concurrently and longitudinally (i.e., six months later). Random forests outperformed all other AIs, while avatar immersion was shown to be the strongest training predictor.
Conclusion: Study outcomes demonstrated that the user-avatar bond can be translated into accurate, concurrent and future GD risk predictions using trained AI classifiers. Assessment, prevention, and practice implications are discussed in the light of these findings.
Databáze: MEDLINE
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