Machine learning and game theory for cybercrime risk assessment in online platform management systems

A proactive framework for cybercrime risk assessment, incorporated using machine learning algorithms and game theory, analyzes platform content moderation effectiveness. With machine learning algorithms, including K-means clustering, ridge regression, interaction analysis, and optimization of Nash equilibrium in 27 quarterly platform analyses, this research proposes four categories of content risks differentiated by systematic differences in levels of threat. The strongest predictor in this sample (β = 0.63) for effectiveness is AI capabilities, accounting for 56.2% of explained variations, although effectiveness is substantially diminished by complexity in moderated content. Optimal automation rates vary from 78% for low complexity to sophisticated approaches at only 29%, offering 55% cost savings and a 54.5% decrease in breaches for low complexity, but risking higher degrees of threat for complex moderated contents. This study suggests that technological development is imperative to supplement resource development for platforms in content moderation approaches.