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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2502.14718 |
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| _version_ | 1866915345035427840 |
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| author | Mahmoud, Tarek Xie, Zhuohan Dimitrov, Dimitar Nikolaidis, Nikolaos Silvano, Purificação Yangarber, Roman Sharma, Shivam Sartori, Elisa Stefanovitch, Nicolas Martino, Giovanni Da San Piskorski, Jakub Nakov, Preslav |
| author_facet | Mahmoud, Tarek Xie, Zhuohan Dimitrov, Dimitar Nikolaidis, Nikolaos Silvano, Purificação Yangarber, Roman Sharma, Shivam Sartori, Elisa Stefanovitch, Nicolas Martino, Giovanni Da San Piskorski, Jakub Nakov, Preslav |
| contents | We introduce a novel multilingual 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14718 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Entity Framing and Role Portrayal in the News Mahmoud, Tarek Xie, Zhuohan Dimitrov, Dimitar Nikolaidis, Nikolaos Silvano, Purificação Yangarber, Roman Sharma, Shivam Sartori, Elisa Stefanovitch, Nicolas Martino, Giovanni Da San Piskorski, Jakub Nakov, Preslav Computation and Language We introduce a novel multilingual 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. |
| title | Entity Framing and Role Portrayal in the News |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.14718 |