@inproceedings{shahzad-etal-2026-upr,
    title = "{UPR} at {S}em{E}val-2026 Task 9: Multi-Label Classification of Polarization Across Social Dimensions and Manifestation Identification in {U}rdu",
    author = "Shahzad, Mtayyaba  and
      Khadam, Inzmam  and
      Mahmood, Zaufishan  and
      Rashid, Junaid  and
      Hayat, Shamaila  and
      Ayub, Fakhar",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.333/",
    pages = "2642--2647",
    ISBN = "979-8-89176-414-9",
    abstract = "The analysis of polarized content on social networks is crucial for understanding public discourse; however, research on low-resource languages such as Urdu remains limited. In this work, we address two complementary subtasks of polarization analysis in Urdu social media text. First, we formulate polarization classification across multiple social dimensions as a multi-label task, including political, religious, racial/ethnic, gender/sexual, and other. We fine-tune XLM-RoBERTa for multi-label classification with language-specific preprocessing, duplicate filtering, and data augmentation to handle class imbalance. The proposed model achieves a Macro F1-score of 0.758 for social-dimension polarization classification.Second, we perform polarization manifestation identification, focusing on how polarization is expressed in text through six manifestations: stereotype, vilification, dehumanization, extreme language, lack of empathy, and invalidation. Using the same transformer-based framework with imbalance-aware training, our system achieves a Macro F1-score of 0.72 on the official test set. These results demonstrate the effectiveness of multilingual transformer models for multi-dimensional polarization analysis in low-resource Urdu text."
}