@inproceedings{raza-etal-2025-slur,
    title = "Slur and Emoji Aware Models for Hate and Sentiment Detection in {R}oman {U}rdu Transgender Discourse",
    author = "Raza, Muhammad Owais  and
      Umar, Aqsa  and
      Awan, Mehrub",
    editor = "Estevanell-Valladares, Ernesto Luis  and
      Picazo-Izquierdo, Alicia  and
      Ranasinghe, Tharindu  and
      Mikaberidze, Besik  and
      Ostermann, Simon  and
      Gurgurov, Daniil  and
      Mueller, Philipp  and
      Borg, Claudia  and
      {\v{S}}imko, Mari{\'a}n",
    booktitle = "Proceedings of the First Workshop on Advancing NLP for Low-Resource Languages",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.lowresnlp-1.14/",
    pages = "131--139",
    abstract = "The rise of social media has amplified both the visibility and vulnerability of marginalized communities, particularly the transgender population in South Asia. While hate speech detection has seen considerable progress in high resource languages like English, under-resourced and code mixed languages such as Roman Urdu remains significantly understudied. This paper presents a novel Roman Urdu dataset derived from Instagram comments on transgender related content, capturing the intricacies of multilingual, code-mixed, and emoji-laden social discourse. We introduce a transphobic slur lexicon specific to Roman Urdu and a semantic emoji taxonomy grounded in contextual usage. These resources are utilized to perform fine-grained classification of sentiment and hate speech using both traditional machine learning models and transformer-based architectures. The findings show that our custom-trained BERT-based models, Senti-RU-Bert and Hate-RU-Bert, best performance, with F1 scores of 80.39{\%} for sentiment classification and 77.34{\%} for hate speech classification. Ablation studies reveal consistent performance gains when slur and emoji features are included."
}