@inproceedings{ghimire-etal-2026-linus,
    title = "{LINUS}@{EEUCA} 2026: Fine-grained Toxicity Detection in Gaming Chat using Multilingual Transformers",
    author = "Ghimire, Prajwal  and
      Mahato, Aashish  and
      Regmi, Sunil",
    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-1.24/",
    pages = "216--222",
    ISBN = "979-8-89176-402-6",
    abstract = "The detection of toxic behavior in online gaming communities is crucial for maintaining safe digital spaces, yet remains challenging due to subtle context-dependent and intent-driven language. The GameTox dataset consists of around 53K World of Tanks chat utterances annotated across six categories: Non-toxic, Insults and Flaming, Other Offensive Texts, Hate and Harassment, Threats, and Extremism (CITATION). Our best performing approach, across multiple transformer-based architecture experimentations, is based on the multilingual BERT variant mmBERT-base fine-tuned with class-weighted cross-entropy loss. The best mmBERT-base model achieved a Macro F1 of 0.5882 during validation and an official test Macro F1 of 0.5104 on the shared task leaderboard. An internal held-out evaluation on a development split yielded 0.4282, which we analyze to understand distributional sensitivity to gaming slang and class imbalance. The code is available at: \url{https://github.com/sunilRegmi-ai/eeuca-toxicity-detection}."
}