通过用户与虚拟形象的联系运用机器学习研究游戏障碍：迈向概念和方法清晰性的一步。
Machine Learning(s) in gaming disorder through the user-avatar bond: A step towards conceptual and methodological clarity.
作者信息
Stavropoulos Vasileios, Prokofieva Maria, Zarate Daniel, Colder Carras Michelle, Ratan Rabindra, Kowert Rachel, Schivinski Bruno, Burleigh Tyrone L, Poulus Dylan, Karimi Leila, Gorman-Alesi Angela, Brown Taylor, Gomez Rapson, Hein Kaiden, Arachchilage Nalin, Griffiths Mark D
机构信息
1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia.
2Victoria University, Australia.
出版信息
J Behav Addict. 2024 Nov 22;13(4):894-900. doi: 10.1556/2006.2024.00063. Print 2024 Dec 30.
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.
针对我们的研究，因凡蒂等人（2024年）的评论提出了关于以下方面的关键问题：（i）用户-虚拟形象纽带在解决游戏障碍（GD）风险方面的概念化及效用；（ii）应用于评估GD风险的监督机器学习技术的优化。为推动这些领域的科学对话与进展，本文旨在：（i）在更广泛的行为成瘾领域内，提高对虚拟形象、用户-虚拟形象纽带以及与游戏障碍（GD）相关的数字表型概念的清晰度和理解；（ii）通过在数据拆分前去除数据增强以及在编程中实施替代数据不平衡处理方法，比较评估用户-虚拟形象纽带（UAB）如何预测GD风险。
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