@inproceedings{piskorski-etal-2025-semeval,
title = "{S}em{E}val 2025 Task 10: Multilingual Characterization and Extraction of Narratives from Online News",
author = "Piskorski, Jakub and
Mahmoud, Tarek and
Nikolaidis, Nikolaos and
Campos, Ricardo and
Mario Jorge, Alipio and
Dimitrov, Dimitar and
Silvano, Purifica{\c{c}}{\~a}o and
Yangarber, Roman and
Sharma, Shivam and
Chakraborty, Tanmoy and
Guimaraes, Nuno and
Sartori, Elisa and
Stefanovitch, Nicolas and
Xie, Zhuohan and
Nakov, Preslav and
Da San Martino, Giovanni",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.331/",
pages = "2610--2643",
ISBN = "979-8-89176-273-2",
abstract = "We introduce SemEval-2025 Task 10 on Multilingual Characterization and Extraction of Narratives from Online News, which focuses on the identification and analysis of narratives in online news media. The task is structured into three subtasks: (1) Entity Framing, to identify the roles that relevant entities play within narratives, (2) Narrative Classification, to assign documents fine-grained narratives according to a given, topic-specific taxonomy of narrative labels, and (3) Narrative Extraction, to provide a justification for the dominant narrative of the document. To this end, we analyze news articles across two critical domains, Ukraine-Russia War and Climate Change, in five languages: Bulgarian, English, Hindi, Portuguese, and Russian. This task introduces a novel multilingual and multifaceted framework for studying how online news media construct and disseminate manipulative narratives. By addressing these challenges, our work contributes to the broader effort of detecting, understanding, and mitigating the spread of propaganda and disinformation. The task attracted a lot of interest: 310 teams registered, with 66 submitting official results on the test set."
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<abstract>We introduce SemEval-2025 Task 10 on Multilingual Characterization and Extraction of Narratives from Online News, which focuses on the identification and analysis of narratives in online news media. The task is structured into three subtasks: (1) Entity Framing, to identify the roles that relevant entities play within narratives, (2) Narrative Classification, to assign documents fine-grained narratives according to a given, topic-specific taxonomy of narrative labels, and (3) Narrative Extraction, to provide a justification for the dominant narrative of the document. To this end, we analyze news articles across two critical domains, Ukraine-Russia War and Climate Change, in five languages: Bulgarian, English, Hindi, Portuguese, and Russian. This task introduces a novel multilingual and multifaceted framework for studying how online news media construct and disseminate manipulative narratives. By addressing these challenges, our work contributes to the broader effort of detecting, understanding, and mitigating the spread of propaganda and disinformation. The task attracted a lot of interest: 310 teams registered, with 66 submitting official results on the test set.</abstract>
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%0 Conference Proceedings
%T SemEval 2025 Task 10: Multilingual Characterization and Extraction of Narratives from Online News
%A Piskorski, Jakub
%A Mahmoud, Tarek
%A Nikolaidis, Nikolaos
%A Campos, Ricardo
%A Mario Jorge, Alipio
%A Dimitrov, Dimitar
%A Silvano, Purificação
%A Yangarber, Roman
%A Sharma, Shivam
%A Chakraborty, Tanmoy
%A Guimaraes, Nuno
%A Sartori, Elisa
%A Stefanovitch, Nicolas
%A Xie, Zhuohan
%A Nakov, Preslav
%A Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F piskorski-etal-2025-semeval
%X We introduce SemEval-2025 Task 10 on Multilingual Characterization and Extraction of Narratives from Online News, which focuses on the identification and analysis of narratives in online news media. The task is structured into three subtasks: (1) Entity Framing, to identify the roles that relevant entities play within narratives, (2) Narrative Classification, to assign documents fine-grained narratives according to a given, topic-specific taxonomy of narrative labels, and (3) Narrative Extraction, to provide a justification for the dominant narrative of the document. To this end, we analyze news articles across two critical domains, Ukraine-Russia War and Climate Change, in five languages: Bulgarian, English, Hindi, Portuguese, and Russian. This task introduces a novel multilingual and multifaceted framework for studying how online news media construct and disseminate manipulative narratives. By addressing these challenges, our work contributes to the broader effort of detecting, understanding, and mitigating the spread of propaganda and disinformation. The task attracted a lot of interest: 310 teams registered, with 66 submitting official results on the test set.
%U https://aclanthology.org/2025.semeval-1.331/
%P 2610-2643
Markdown (Informal)
[SemEval 2025 Task 10: Multilingual Characterization and Extraction of Narratives from Online News](https://aclanthology.org/2025.semeval-1.331/) (Piskorski et al., SemEval 2025)
ACL
- Jakub Piskorski, Tarek Mahmoud, Nikolaos Nikolaidis, Ricardo Campos, Alipio Mario Jorge, Dimitar Dimitrov, Purificação Silvano, Roman Yangarber, Shivam Sharma, Tanmoy Chakraborty, Nuno Guimaraes, Elisa Sartori, Nicolas Stefanovitch, Zhuohan Xie, Preslav Nakov, and Giovanni Da San Martino. 2025. SemEval 2025 Task 10: Multilingual Characterization and Extraction of Narratives from Online News. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 2610–2643, Vienna, Austria. Association for Computational Linguistics.