G ame T ox: A Comprehensive Dataset and Analysis for Enhanced Toxicity Detection in Online Gaming Communities (2025)

Abstract: The prevalence of toxic behavior in online gaming communities necessitates robust detection methods to ensure user safety. We introduce GameTox, a novel dataset comprising 53K game chat utterances annotated for toxicity detection through intent classification and slot filling. This dataset captures the complex relationship between user intent and specific linguistic features that contribute to toxic interactions. We extensively analyze the dataset to uncover key insights into the nature of toxic speech in gaming environments. Furthermore, we establish baseline performance metrics using state-of-the-art natural language processing and large language models, demonstrating the dataset’s contribution towards enhancing the detection of toxic behavior and revealing the limitations of contemporary models. Our results indicate that leveraging both intent detection and slot filling provides a significantly more granular and context-aware understanding of harmful messages. This dataset serves as a valuable resource to train advanced models that can effectively mitigate toxicity in online gaming and foster healthier digital spaces. Our dataset is publicly available at: https://github.com/shucoll/GameTox.

## introduction the rapid expansion of online gaming has revolutionized entertainment, creating dynamic and engaging experiences for players worldwide. however, with this growth arises the challenge of maintaining a safe environment amidst a backdrop of increasingly toxic behavior @xcite . toxic behavior refers to negative actions by players that harm the gaming experience for others, such as harassment, griefing, or aggressive communication @xcite , which can significantly detract from the user experience and lead to psychological harm @xcite . several techniques have been used to manage toxic speech in online games and promote a positive online environment. these include word censorship, shadow banning users, and restricting their ability to communicate @xcite . while efforts have been made to develop frameworks and curate datasets to advance automated toxicity detection in online games, current datasets focus only on utterance-level annotation @xcite @xcite . while utterance-level annotation of samples is intuitively reasonable for intent classification, using only one label for long sequences can lead to ambiguity and misclassification @xcite , especially in online interactions which typically use a large amount of metaphors and slang @xcite . slot filling, or the annotation of each word in a sentence, has emerged as a promising method in natural language processing (nlp) as it offers an abundance of labels for data-hungry deep learning models. further, slot filling facilitates the extraction of semantic concepts from text sequences, which improves the generalization ability of language models @xcite . the addition of token-level labels enhances the performance of models for tasks such as utterance-level classification @xcite . however, despite the benefits of joint task datasets spanning both intent classification and slot filling, data resources in this field remain limited. to address these gaps, we propose gametox, a toxicity detection dataset consisting of 53,000 online game chats from the game world of tanks (wot) collected through the wot-record 1 database. the data comprises manual annotations for 6 classes at the utterance level (intent classification) and automated lexicon-based annotations for 4 classes at the token level (slot filling). with game-tox, we aim to facilitate the development of robust and granular toxicity detection models, ultimately contributing to safer online gaming communities. ## conclusion in this work, we introduce gametox, a dataset for toxicity intent detection and slot filling in gaming environments. our dataset is unique in its dual focus, capturing both the intentions behind toxic utterances and the specific components of speech that contribute to toxicity. we conducted baseline classification experiments using state-of-the-art nlp models, validating the dataset's utility in both intent detection and slot-filling tasks. our experiments provide a benchmark for future research, highlighting the dataset's potential to enhance the precision and depth of toxicity detection methods. with ga-metox, we aim to foster further innovation in the development of sophisticated, context-aware toxicity detection systems. future work can focus on expanding the dataset, refining these models, and exploring their applications across diverse online platforms to mitigate toxic interactions and promote healthier online communities.