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

Vasileios Stavropoulos^1,2, Daniel Zarate¹, Maria Prokofieva³

¹1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia.

Journal of Behavioral Addictions

|November 9, 2023

Summary

The user-avatar bond accurately predicts gaming disorder (GD) risk using artificial intelligence (AI). AI models identified GD risk based on gamer-avatar connection, age, and gaming duration, showing potential for early assessment and prevention.

Keywords:

artificial intelligence avatar gaming disorder machine learning online gaming user-avatar bond

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Area of Science:

Psychology
Computer Science
Mental Health

Background:

Gaming disorder (GD) risk is linked to the gamer's bond with their in-game avatar.
The user-avatar relationship can offer insights into a gamer's mental health and GD risk.

Purpose of the Study:

To investigate the predictive power of the user-avatar bond on gaming disorder risk.
To explore the use of artificial intelligence (AI) in assessing concurrent and prospective GD risk.

Main Methods:

565 gamers were assessed twice over six months using the User-Avatar-Bond Scale (UABS) and the Gaming Disorder Test.
Tuned and untuned AI classifiers analyzed gamer responses for concurrent and longitudinal GD risk prediction.

Main Results:

AI models accurately identified GD risk based on UABS scores, age, and gaming duration.
Avatar immersion was the strongest predictor in AI models; random forests showed superior performance.
AI successfully predicted GD risk concurrently and up to six months prospectively.

Conclusions:

The user-avatar bond is a significant factor in predicting gaming disorder risk.
AI classifiers can effectively translate user-avatar bond metrics into actionable GD risk assessments.
Findings support implications for GD assessment, prevention, and intervention strategies.