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

arXiv: https://arxiv.org/abs/2604.10175 · HTML: https://mlsanity.com/paper/2604.10175/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 76 references that are in the map.
- First posted on arXiv 11 Apr 2026.

## Builds on (most cited first, 16 of 16)

- [Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes](https://mlsanity.com/paper/2305.02301/) (2023)
- [ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter](https://mlsanity.com/paper/2008.06465/) (2020)
- [MuTox: Universal MUltilingual Audio-based TOXicity Dataset and Zero-shot Detector](https://mlsanity.com/paper/2401.05060/) (2024)
- [CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection](https://mlsanity.com/paper/2106.06213/) (2021)
- [DeToxy: A Large-Scale Multimodal Dataset for Toxicity Classification in Spoken Utterances](https://mlsanity.com/paper/2110.07592/) (2021)
- [Exploring Cyberbullying and Other Toxic Behavior in Team Competition Online Games](https://mlsanity.com/paper/1504.02305/) (2015)
- [A Machine Learning Approach to Comment Toxicity Classification](https://mlsanity.com/paper/1903.06765/) (2019)
- [Machine learning and semantic analysis of in-game chat for cyberbullying](https://mlsanity.com/paper/1907.10855/) (2019)
- [Prediction Uncertainty Estimation for Hate Speech Classification](https://mlsanity.com/paper/1909.07158/) (2019)
- [STFU NOOB! Predicting Crowdsourced Decisions on Toxic Behavior in Online Games](https://mlsanity.com/paper/1404.5905/) (2014)
- [Attribute Inference Attacks in Online Multiplayer Video Games: a Case Study on Dota2](https://mlsanity.com/paper/2210.09028/) (2022)
- [Understanding the Process of Data Labeling in Cybersecurity](https://mlsanity.com/paper/2311.16388/) (2023)
- [Benchmarking Quantized Neural Networks on FPGAs with FINN](https://mlsanity.com/paper/2102.01341/) (2021)
- [The Dark Side of the Web: Towards Understanding Various Data Sources in Cyber Threat Intelligence](https://mlsanity.com/paper/2504.14235/) (2025)
- [Recognizing Affiliation: Using Behavioural Traces to Predict the Quality of Social Interactions in Online Games](https://mlsanity.com/paper/2003.03438/) (2020)
- [Automatically Detecting Cyberbullying Comments on Online Game Forums](https://mlsanity.com/paper/2106.01598/) (2021)

## Abstract (arXiv)

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.