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)

Stavropoulos, V, Prokofieva, M, Zarate, D, Carras, MC, Ratan, R, Kowert, R, Schivinski, B, Burleigh, TL ORCID: https://orcid.org/0000-0002-3405-140X, Poulus, D, Karimi, L, Gorman-Alesi, A, Brown, T, Gomez, R, Hein, K, Arachchilage, N and Griffiths, MD ORCID: https://orcid.org/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. ISSN 2062-5871

Preview

Text 2298927_Griffiths.pdf - Published version Download (1MB) | Preview

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.

Item Type:	Journal article
Publication Title:	Journal of Behavioral Addictions
Creators:	Stavropoulos, V., Prokofieva, M., Zarate, D., Carras, M.C., Ratan, R., Kowert, R., Schivinski, B., Burleigh, T.L., Poulus, D., Karimi, L., Gorman-Alesi, A., Brown, T., Gomez, R., Hein, K., Arachchilage, N. and Griffiths, M.D.
Publisher:	Akadémiai Kiadó
Date:	22 November 2024
ISSN:	2062-5871
Identifiers:	Number Type 10.1556/2006.2024.00063 DOI 2298927 Other
Rights:	This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium for non-commercial purposes, provided the original author and source are credited, a link to the CC License is provided, and changes – if any – are indicated.
Divisions:	Schools > School of Social Sciences
Record created by:	Jonathan Gallacher
Date Added:	25 Nov 2024 14:02
Last Modified:	25 Nov 2024 14:02
URI:	https://irep.ntu.ac.uk/id/eprint/52653

Actions (login required)

Edit View

Statistics

Views

Downloads