@inproceedings{naseem-etal-2025-gametox,
title = "{G}ame{T}ox: A Comprehensive Dataset and Analysis for Enhanced Toxicity Detection in Online Gaming Communities",
author = "Naseem, Usman and
Shiwakoti, Shuvam and
Shah, Siddhant Bikram and
Thapa, Surendrabikram and
Zhang, Qi",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-short.37/",
pages = "440--447",
ISBN = "979-8-89176-190-2",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T GameTox: A Comprehensive Dataset and Analysis for Enhanced Toxicity Detection in Online Gaming Communities
%A Naseem, Usman
%A Shiwakoti, Shuvam
%A Shah, Siddhant Bikram
%A Thapa, Surendrabikram
%A Zhang, Qi
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-190-2
%F naseem-etal-2025-gametox
%X 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.
%U https://aclanthology.org/2025.naacl-short.37/
%P 440-447
Markdown (Informal)
[GameTox: A Comprehensive Dataset and Analysis for Enhanced Toxicity Detection in Online Gaming Communities](https://aclanthology.org/2025.naacl-short.37/) (Naseem et al., NAACL 2025)
ACL