@inproceedings{thapa-etal-2026-understanding,
    title = "Understanding Toxic Behavior in Gaming Communities Using {AI} to Promote Healthier Digital Spaces",
    author = {Thapa, Surendrabikram  and
      Shiwakoti, Shuvam  and
      Shah, Siddhant Bikram  and
      Rauniyar, Kritesh  and
      Thapa, Laxmi  and
      Adhikari, Surabhi  and
      Johnson, Kristina T.  and
      H{\"u}rriyeto{\u{g}}lu, Ali  and
      Tanev, Hristo  and
      Naseem, Usman},
    editor = {H{\"u}rriyeto{\u{g}}lu, Ali  and
      Thapa, Surendrabikram  and
      Tanev, Hristo},
    booktitle = "Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications ({EEUCA} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.eeuca-1.2/",
    pages = "8--16",
    ISBN = "979-8-89176-402-6",
    abstract = "Online gaming communities are increasingly affected by toxic communication, including harassment, threats, hate speech, and extremist content. Detecting such behavior is challenging due to the short, noisy, multilingual, and highly imbalanced nature of gaming chat data. To advance research in this area, we organized the Shared Task on Fine-Grained Toxicity Detection in Online Gaming at EEUCA 2026, co-located with ACL 2026. The task is based on the GameTox dataset, containing approximately 53,000 annotated chat utterances from World of Tanks across six toxicity categories. A total of 102 participants took part, and 35 teams submitted systems exploring approaches such as domain-adaptive pretraining, multilingual transfer learning, contrastive learning, LLM-based augmentation, and ensemble methods. Systems were evaluated using macro-averaged F1-score, with the top system achieving 0.7041 Macro F1. This paper presents an overview of the shared task, dataset, evaluation framework, participant methods, and key findings."
}