@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://aclanthology.org/2025.findings-acl.17/",
doi = "10.18653/v1/2025.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."
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%0 Conference Proceedings
%T Entity Framing and Role Portrayal in the News
%A Mahmoud, Tarek
%A Xie, Zhuohan
%A Dimitrov, Dimitar Iliyanov
%A Nikolaidis, Nikolaos
%A Silvano, Purificação
%A Yangarber, Roman
%A Sharma, Shivam
%A Sartori, Elisa
%A Stefanovitch, Nicolas
%A Da San Martino, Giovanni
%A Piskorski, Jakub
%A Nakov, Preslav
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F mahmoud-etal-2025-entity
%X 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.
%R 10.18653/v1/2025.findings-acl.17
%U https://aclanthology.org/2025.findings-acl.17/
%U https://doi.org/10.18653/v1/2025.findings-acl.17
%P 302-326
Markdown (Informal)
[Entity Framing and Role Portrayal in the News](https://aclanthology.org/2025.findings-acl.17/) (Mahmoud et al., Findings 2025)
ACL
- Tarek Mahmoud, Zhuohan Xie, Dimitar Iliyanov Dimitrov, Nikolaos Nikolaidis, Purificação Silvano, Roman Yangarber, Shivam Sharma, Elisa Sartori, Nicolas Stefanovitch, Giovanni Da San Martino, Jakub Piskorski, and Preslav Nakov. 2025. Entity Framing and Role Portrayal in the News. In Findings of the Association for Computational Linguistics: ACL 2025, pages 302–326, Vienna, Austria. Association for Computational Linguistics.