@inproceedings{guimaraes-etal-2025-narratex,
title = "{N}arrat{EX} Dataset: Explaining the Dominant Narratives in News Texts",
author = "Guimar{\~a}es, Nuno and
Silvano, Purifica{\c{c}}{\~a}o and
Campos, Ricardo and
Jorge, Alipio and
Pacheco, Ana Filipa and
Dimitrov, Dimitar Iliyanov and
Nikolaidis, Nikolaos and
Yangarber, Roman and
Sartori, Elisa and
Stefanovitch, Nicolas and
Nakov, Preslav and
Piskorski, Jakub and
Da San Martino, Giovanni",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1112/",
doi = "10.18653/v1/2025.findings-emnlp.1112",
pages = "20408--20434",
ISBN = "979-8-89176-335-7",
abstract = "We present NarratEX, a dataset designed for the task of explaining the choice of the Dominant Narrative in a news article, and intended to support the research community in addressing challenges such as discourse polarization and propaganda detection. Our dataset comprises 1,056 news articles in four languages, Bulgarian, English, Portuguese, and Russian, covering two globally significant topics: the Ukraine-Russia War (URW) and Climate Change (CC). Each article is manually annotated with a dominant narrative and sub-narrative labels, and an explanation justifying the chosen labels. We describe the dataset, the process of its creation, and its characteristics. We present experiments with two new proposed tasks: Explaining Dominant Narrative based on Text, which involves writing a concise paragraph to justify the choice of the dominant narrative and sub-narrative of a given text, and Inferring Dominant Narrative from Explanation, which involves predicting the appropriate dominant narrative category based on an explanatory text. The proposed dataset is a valuable resource for advancing research on detecting and mitigating manipulative content, while promoting a deeper understanding of how narratives influence public discourse."
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<abstract>We present NarratEX, a dataset designed for the task of explaining the choice of the Dominant Narrative in a news article, and intended to support the research community in addressing challenges such as discourse polarization and propaganda detection. Our dataset comprises 1,056 news articles in four languages, Bulgarian, English, Portuguese, and Russian, covering two globally significant topics: the Ukraine-Russia War (URW) and Climate Change (CC). Each article is manually annotated with a dominant narrative and sub-narrative labels, and an explanation justifying the chosen labels. We describe the dataset, the process of its creation, and its characteristics. We present experiments with two new proposed tasks: Explaining Dominant Narrative based on Text, which involves writing a concise paragraph to justify the choice of the dominant narrative and sub-narrative of a given text, and Inferring Dominant Narrative from Explanation, which involves predicting the appropriate dominant narrative category based on an explanatory text. The proposed dataset is a valuable resource for advancing research on detecting and mitigating manipulative content, while promoting a deeper understanding of how narratives influence public discourse.</abstract>
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%0 Conference Proceedings
%T NarratEX Dataset: Explaining the Dominant Narratives in News Texts
%A Guimarães, Nuno
%A Silvano, Purificação
%A Campos, Ricardo
%A Jorge, Alipio
%A Pacheco, Ana Filipa
%A Dimitrov, Dimitar Iliyanov
%A Nikolaidis, Nikolaos
%A Yangarber, Roman
%A Sartori, Elisa
%A Stefanovitch, Nicolas
%A Nakov, Preslav
%A Piskorski, Jakub
%A Da San Martino, Giovanni
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F guimaraes-etal-2025-narratex
%X We present NarratEX, a dataset designed for the task of explaining the choice of the Dominant Narrative in a news article, and intended to support the research community in addressing challenges such as discourse polarization and propaganda detection. Our dataset comprises 1,056 news articles in four languages, Bulgarian, English, Portuguese, and Russian, covering two globally significant topics: the Ukraine-Russia War (URW) and Climate Change (CC). Each article is manually annotated with a dominant narrative and sub-narrative labels, and an explanation justifying the chosen labels. We describe the dataset, the process of its creation, and its characteristics. We present experiments with two new proposed tasks: Explaining Dominant Narrative based on Text, which involves writing a concise paragraph to justify the choice of the dominant narrative and sub-narrative of a given text, and Inferring Dominant Narrative from Explanation, which involves predicting the appropriate dominant narrative category based on an explanatory text. The proposed dataset is a valuable resource for advancing research on detecting and mitigating manipulative content, while promoting a deeper understanding of how narratives influence public discourse.
%R 10.18653/v1/2025.findings-emnlp.1112
%U https://aclanthology.org/2025.findings-emnlp.1112/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1112
%P 20408-20434
Markdown (Informal)
[NarratEX Dataset: Explaining the Dominant Narratives in News Texts](https://aclanthology.org/2025.findings-emnlp.1112/) (Guimarães et al., Findings 2025)
ACL
- Nuno Guimarães, Purificação Silvano, Ricardo Campos, Alipio Jorge, Ana Filipa Pacheco, Dimitar Iliyanov Dimitrov, Nikolaos Nikolaidis, Roman Yangarber, Elisa Sartori, Nicolas Stefanovitch, Preslav Nakov, Jakub Piskorski, and Giovanni Da San Martino. 2025. NarratEX Dataset: Explaining the Dominant Narratives in News Texts. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 20408–20434, Suzhou, China. Association for Computational Linguistics.