Challenge: a new dataset of news articles annotated for narratives provides a framework for narrative detection . recurring narratives can propagate with very high velocity across audiences, languages and countries .
Approach: They propose a multilingual dataset annotated for narratives using two-level taxonomies . they define narrative as a recurring, repetitive, overt or implicit claim that promotes a specific interpretation or viewpoint on an ongoing topic .
Outcome: The proposed dataset will foster research in narrative detection and enable new research directions . the authors identify multiple narratives in the same article, and the results are published online .

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Entity Framing and Role Portrayal in the News (2025.findings-acl)

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Challenge: a dataset of news articles containing 22 fine-grained characters is annotated for entity framing and role portrayal . the dataset includes 1,378 recent news articles in five languages focusing on the Ukraine-Russia War and climate change .
Approach: They propose a multilingual and hierarchical corpus annotated for entity framing and role portrayal in news articles.
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Text2Story Lusa: A Dataset for Narrative Analysis in European Portuguese News Articles (2024.lrec-main)

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Challenge: Access to annotated corpora with narrative elements is limited due to the lack of readily available datasets and copyright concerns.
Approach: They developed a dataset that contains 357 news articles and 117 manually annotated articles with over 50 thousand individual annotations.
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PartisanLens: A Multilingual Dataset of Hyperpartisan and Conspiratorial Immigration Narratives in European Media (2026.eacl-long)

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Challenge: Existing methods for detecting hyperpartisan narratives and PRCTs are limited . hyperpartisan content promotes extreme views through one-sided, emotional language .
Approach: They propose a multilingual dataset of 1617 hyperpartisan news headlines in Spanish, Italian, and Portuguese annotated in multiple political discourse aspects.
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Narratives at Conflict: Computational Analysis of News Framing in Multilingual Disinformation Campaigns (2024.acl-srw)

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Challenge: Existing methods for multilingual framing differ from those used in English-speaking world . framers often use loaded vocabularies to create political images or favor a particular point of view .
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Multilingual Multifaceted Understanding of Online News in Terms of Genre, Framing, and Persuasion Techniques (2023.acl-long)

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Challenge: a new dataset of news articles is presented that covers genre, framing, and persuasion techniques.
Approach: They propose a multilingual multifacet dataset of news articles annotated for genre, framing and persuasion techniques.
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A Study on Scaling Up Multilingual News Framing Analysis (2024.findings-naacl)

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Challenge: Existing studies on media framing have focused on English only data, leaving a gap in research concerning multilingual contexts.
Approach: They propose to use crowd-sourced datasets to automate framing analysis by automating translation and annotation.
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text2story: A Python Toolkit to Extract and Visualize Story Components of Narrative Text (2024.lrec-main)

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Challenge: Story components, namely events, time, participants, and their relations, are present in narrative texts from different domains such as journalism, medicine, finance, and law.
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Detecting Narrative Elements in Informational Text (2022.findings-naacl)

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Challenge: Recent work has focused on identifying narrative elements in personal stories texts, but this paper focuses on informational texts.
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A Dataset for Multi-lingual Epidemiological Event Extraction (2020.lrec-1)

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Challenge: Using the Web, we propose a corpus for information extraction and text classification.
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Outcome: The proposed corpus can be used for information extraction and natural language processing tasks such as text classification.
Multi-Label and Multilingual News Framing Analysis (2020.acl-main)

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Challenge: Recent studies have focused on news framing in English, but few studies have explored how it can be extended to other languages and in multi-label settings.
Approach: They propose a method that leverages dictionary and few annotations to detect frames from just the headline in a low-resource context.
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