@inproceedings{mahmoud-etal-2025-entity,
    title = "Entity Framing and Role Portrayal in the News",
    author = "Mahmoud, Tarek  and
      Xie, Zhuohan  and
      Dimitrov, Dimitar Iliyanov  and
      Nikolaidis, Nikolaos  and
      Silvano, Purifica{\c{c}}{\~a}o  and
      Yangarber, Roman  and
      Sharma, Shivam  and
      Sartori, Elisa  and
      Stefanovitch, Nicolas  and
      Da San Martino, Giovanni  and
      Piskorski, Jakub  and
      Nakov, Preslav",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.17/",
    pages = "302--326",
    ISBN = "979-8-89176-256-5",
    abstract = "We introduce a novel multilingual and hierarchical corpus annotated for entity framing and role portrayal in news articles. The dataset uses a unique taxonomy inspired by storytelling elements, comprising 22 fine-grained roles, or archetypes, nested within three main categories: protagonist, antagonist, and innocent. Each archetype is carefully defined, capturing nuanced portrayals of entities such as guardian, martyr, and underdog for protagonists; tyrant, deceiver, and bigot for antagonists; and victim, scapegoat, and exploited for innocents. The dataset includes 1,378 recent news articles in five languages (Bulgarian, English, Hindi, European Portuguese, and Russian) focusing on two critical domains of global significance: the Ukraine-Russia War and Climate Change. Over 5,800 entity mentions have been annotated with role labels. This dataset serves as a valuable resource for research into role portrayal and has broader implications for news analysis. We describe the characteristics of the dataset and the annotation process, and we report evaluation results on fine-tuned state-of-the-art multilingual transformers and hierarchical zero-shot learning using LLMs at the level of a document, a paragraph, and a sentence."
}