An Evaluation of Chat Safety Moderations in Roblox

Original Abstract

Roblox is among the most popular online gaming platforms, used by hundreds of millions of users every day. A substantial portion of these users are underage, who are at a greater risk, where abusive users may utilize Roblox's real-time chat interface to make the initial contact with potential victims. Roblox employs automated chat moderation mechanisms to detect potentially abusive messages; however, to date, their effectiveness has not been independently investigated. Toward this goal, we collected approximately 2 million chat messages from four games across multiple age groups and analyzed them to evaluate the moderation system. These messages were collected from public game servers following ethical and legal norms as well as Roblox's terms of service. We use this corpus to qualitatively study which types of unsafe chats escape the moderation system and how policy-violating users evade the moderation system. Given the dataset's scale, it is prohibitively expensive to conduct qualitative content analysis manually. Therefore, we adopt a two-step approach. First, we manually labeled safe and unsafe messages (n=99.8K) and used them as a ground truth to evaluate four locally hosted state-of-the-art large language models (LLMs). Next, the best-performing LLM was applied to the entire corpus to identify potentially unsafe messages, which we manually categorized using iterative open and axial coding methods until thematic saturation was reached. Overall, our findings reveal a troublesome reality: numerous instances of unsafe chat messages related to grooming, sexualizing minors, bullying, & harassment, violence, self-harm, and sharing sensitive information, etc., escaped the current moderation. Our analysis of users whose messages were previously flagged revealed that they continue to send harmful messages by employing a wide range of techniques to evade the moderation system.