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A Longitudinal, Multinational, and Multilingual Corpus of News Coverage of the Russo-Ukrainian War

arXiv 2601.16309 ↗First posted 22 Jan 2026cs.CL cs.SIcited by 0

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Abstract (arXiv)

We present DNIPRO, a corpus of 246K news articles from the Russo-Ukrainian war (Feb 2022 -- Aug 2024) spanning eleven outlets across five nation-states (Russia, Ukraine, U.S., U.K., China) and three languages. The corpus features comprehensive metadata and human-evaluated annotations for stance, sentiment, and topical framing, enabling systematic analysis of competing geopolitical narratives. It is uniquely suited for empirical studies of narrative divergence, media framing, and information warfare. Our exploratory analyses reveal how media outlets construct incompatible realities through divergent attribution and topical selection without direct refutation of opposing narratives. DNIPRO empowers empirical research on narrative evolution, cross-lingual information flow, and computational detection of implicit contradictions in fragmented information ecosystems.

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References · 12

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  3. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,312
  4. 2020Beyond English-Centric Multilingual Machine Translationcited by 561
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  6. 2023A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Modelscited by 246
  7. 2020Explainable Automated Fact-Checking for Public Health Claimscited by 211
  8. 2022Democratizing Neural Machine Translation with OPUS-MTcited by 58
  9. 2022Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Mediacited by 10
  10. 2024Can Large Language Models Address Open-Target Stance Detection?cited by 10
  11. 2022A Reddit Dataset for the Russo-Ukrainian Conflict in 2022cited by 6
  12. 2023Unveiling Global Narratives: A Multilingual Twitter Dataset of News Media on the Russo-Ukrainian Conflictcited by 1

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