Cyberbullying among minors is a pressing concern in our digital society, necessitating effective prevention and intervention strategies. Traditional data collection methods often intrude on privacy and yield limited insights. This study explores an innovative approach, employing a serious game - designed with purposes beyond entertainment - as a non-intrusive tool for data collection and education. In contrast to traditional correlation-based analyses, we propose a causality-based approach using Bayesian Networks to unravel complex relationships in the collected data and quantify result uncertainties. This robust analytical tool yields interpretable outcomes, enhances transparency in assumptions, and fosters open scientific discourse. Preliminary pilot studies with the serious game show promising results, surpassing the informative capacity of traditional demographic and psychological questionnaires, suggesting its potential as an alternative methodology. Additionally, we demonstrate how our approach facilitates the examination of risk profiles and the identification of intervention strategies to mitigate this cybercrime. We also address research limitations and potential enhancements, considering the noise and variability of data in social studies and video games. This research advances our understanding of cyberbullying and showcase the potential of serious games and causality-based approaches in studying complex social issues.

翻译：未成年人网络欺凌是数字社会中亟待解决的紧迫问题，亟需有效的预防与干预策略。传统数据收集方法常侵犯隐私且信息获取有限。本研究探索了一种创新方法，采用以娱乐之外目的设计的严肃游戏作为非侵入式数据收集与教育工具。区别于传统基于相关性的分析，我们提出基于贝叶斯网络的因果分析方法，以揭示收集数据中的复杂关系并量化结果的不确定性。这一稳健分析工具可产出可解释结果，增强假设透明度，推动开放的科学讨论。针对该严肃游戏的初步先导研究展现了良好效果，其信息承载能力超越传统人口学与心理学问卷，表明其作为替代方法学的潜力。此外，我们论证了该方法如何促进风险特征分析及识别减缓此类网络犯罪的干预策略。考虑到社会研究与电子游戏数据的噪声与变异性，我们亦探讨了研究局限与潜在改进方向。本研究深化了对网络欺凌的理解，并展示了严肃游戏与因果分析法在研究复杂社会问题中的潜力。