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.

翻译：《英雄联盟》、《反恐精英》和《Skribbl.io》等在线多人游戏通过社区互动创造体验。为玩家提供多种互动模式的能力也如同打开了潘多拉魔盒。毒性行为和恶意玩家可能破坏游戏体验、减少玩家基数，进而损害游戏及工作室的成功前景。本文将从文本、音频与图像处理问题以及行为毒性等方面，简要概述毒性内容检测面临的主要挑战。同时探讨当前企业主导与用户主导的内容检测实践，并分析人工智能时代自动化内容检测的价值与局限。