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

Computer Science > Cryptography and Security [Submitted on 11 Apr 2026] "bot lane noob" Towards Deployment of NLP-based Toxicity Detectors in Video Games Jonas Ave, Irdin Pekaric, Matthias Frohner, Giovanni Apruzzese 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. Comments: Accepted to ESORICS'26 Subjects: Cryptography and Security (cs.CR); Computers and Society (cs.CY); Machine Learning (cs.LG) Cite as: arXiv:2604.10175 [cs.CR]   (or arXiv:2604.10175v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.10175 Focus to learn more Submission history From: Giovanni Apruzzese [view email] [v1] Sat, 11 Apr 2026 12:06:25 UTC (2,406 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.CY cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)