Challenges for Real-Time Toxicity Detection in Online Games

TL;DR

This paper reviews the challenges and current practices in detecting toxic behavior in online multiplayer games across text, audio, and images, highlighting the limitations of automated detection methods amid AI advancements.

Contribution

It provides a comprehensive overview of toxicity detection challenges and evaluates existing practices and limitations in automated content moderation for online games.

Findings

Toxicity detection involves complex multimodal content analysis.

Current automated methods face limitations in accuracy and context understanding.

AI advancements both aid and complicate toxicity moderation efforts.

Abstract

Online multiplayer games like League of Legends, Counter Strike, and Skribbl.io create experiences through community interactions. Providing players with the ability to interact with each other through multiple modes also opens a Pandora box. Toxic behaviour and malicious players can ruin the experience, reduce the player base and potentially harming the success of the game and the studio. This article will give a brief overview of the challenges faced in toxic content detection in terms of text, audio and image processing problems, and behavioural toxicity. It also discusses the current practices in company-directed and user-directed content detection and discuss the values and limitations of automated content detection in the age of artificial intelligence.