Peer-reviewed article
Child Safety and Protection in the Online Gaming Ecosystem
Anum Faraz, Jinane Mounsef, Ali Raza, Sandra Willis ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This paper surveys threats to children in online gaming environments, including cyberbullying, grooming, and sexting. It reviews existing protective measures such as content filtering and parental controls, and examines machine learning techniques designed to detect child predatory behavior. The authors identify gaps in current industry efforts and present recommendations for policymakers and the gaming industry to create safer online spaces for children.
Key points
- Children face multifaceted threats in online gaming including cyberbullying, grooming, and sexting
- Existing industry safety measures remain inadequate and fragmented despite concerted efforts
- Content filtering and parental controls are in use but have limitations in protecting children from predators
- Machine learning techniques show potential for detecting child predatory behavior but face implementation challenges
- There is a need for laws, policies, and minimum standards to regulate the online gaming industry
Limitations
This is a literature review and survey paper that does not present empirical evaluation of specific interventions with measured outcomes. It does not provide data on the effectiveness of any protection measures or ML techniques, nor does it evaluate the impact of policies on children's safety.
Abstract
Online gaming no longer has limited access, as it has become available to a high percentage of children in recent years. Consequently, children are exposed to multifaceted threats, such as cyberbullying, grooming, and sexting. Although the online gaming industry is taking concerted measures to create a safe environment for children to play and interact with, such efforts remain inadequate and fragmented. There is a vital need to develop laws and policies to regulate and build minimum standards for the industry to safeguard and protect children online on the one hand, while promoting innovations in the gaming industry to preempt such threats. Many tools have been adapted to control threats against children in the form of content filtering and parental controls, thereby restricting contact with children to protect them from child predators. Different approaches utilizing machine learning (ML) techniques to detect child predatory behavior have been designed to provide potential detection and protection in this context. In this paper, we survey online threats to children in the gaming environment and present the limitations of existing solutions that address these threats. We also aimed to present the challenges that ML techniques face in protecting children against predatory behavior by presenting a systematic review of the available techniques in the literature. Therefore, this analysis provides not only recommendations to stakeholders to develop policies and practices that safeguard children when gaming, but also to the gaming industry to continue providing appropriate measures for a safe and entertaining gaming environment.