"bot lane noob"Towards Deployment of NLP-based Toxicity Detectors in Video Games

2026-04-11

AI Summary

This study addresses the poor performance of existing general-purpose toxicity detection models in real-time gaming chat by identifying a critical gap in high-quality, fine-grained annotated datasets and domain-specific tools through a systematic literature review. To bridge this gap, the authors collaborated with eight League of Legends experts to construct L2DTnH, a fine-grained dataset comprising 1.4k toxic and 13.8k non-toxic messages. Leveraging this dataset, they trained a specialized NLP toxicity detection model and implemented it as a lightweight browser extension that operates locally without reliance on third-party AI services, enabling real-time in-game intervention. Experimental results demonstrate that the proposed model significantly outperforms both general-purpose and state-of-the-art toxicity detectors in gaming contexts and exhibits strong cross-game generalization. The dataset, model, and tool are publicly released.

Technology Category

• Natural Language Processing: Safety and Robustness
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Toxicity and harassment are widespread in the video-gaming context. Especially in competitive online multiplayer scenarios, gamers oftentimes send harmful messages to other players (teammates or opponents) whose consequences span from mild annoyance to withdrawal and depression. Abundant prior work tackled these problems, e.g., pointing out the negative effects of toxic interactions. However, few works proposed countermeasures specifically developed and tested on textual messages sent during a match -- i.e., when the"harassment"actually occurs. We posit that such a scarcity stems from the lack of high-quality datasets that can be used to devise"automated"detectors based on natural-language processing (NLP) and machine learning (ML), and which can -- ideally -- mitigate the harm of toxic comments during a gaming session. This work provides a foundation for addressing the problem of toxicity and harassment in video games. First, through a systematic literature review (n=1,039), we provide evidence that only few works proposed ML/NLP-based detectors of toxicity/harassment during live matches. Then, we partner-up with 8 expert League of Legend (LoL) players and create a fine-grained labelled dataset, L2DTnH, containing 1.4k toxic and 13.8k non-toxic messages exchanged during LoL matches. We use L2DTnH to develop a detector that we then empirically show outperforms general-purpose and state-of-the-art toxicity detectors reliant on NLP. To further demonstrate the practicality of our resources, we test our detector on game-related data beyond that included in L2DTnH; and we develop a Web-browser extension that flags toxic content in Webpages -- without querying third-party servers owned by AI companies. We publicly release all of our resources. Our contributions pave the way for more applied research devoted to fighting the spread of toxicity and harassment in video games.

Problem

• toxicity
• harassment
• video games
• NLP
• online multiplayer

Innovation

• toxicity detection
• NLP
• gaming dataset
• real-time moderation
• privacy-preserving deployment