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

Summary

Research on how rival states narrate the Russo-Ukrainian war has lacked long-running news datasets that span several countries and languages. Most existing resources are social-media collections, whose post identifiers decay quickly. News corpora such as RUWA are small or limited to specific events. The authors build dnipro, a corpus of 246,229 articles collected daily from 1 February 2022 to 31 August 2024. The articles come from 11 outlets tied to Russia, Ukraine, the U.S., the U.K. and China, and are written in English, Russian and Mandarin Chinese. Articles were selected through dedicated war sections, conflict tags or keyword filters, checked with stratified manual sampling, and stored under a unified metadata schema. The corpus adds annotations for topical framing and for entity-level stance and sentiment, validated by human annotators. Exploratory analyses suggest that outlets construct mutually incompatible versions of events. They do this by choosing which topics to cover and whom to credit or blame, rather than openly rebutting the other side. dnipro does not fact-check claims. Instead, it supports empirical work on media framing, information warfare and cross-lingual narrative flow.

Key points

  • Scale: 246,229 articles over 31 months, averaging about 261 per day. 61,637 articles (25.03%) are non-English. Average article length after translation into English is about 332.5 words.
  • Sources: CNN (5,760 articles) and the Financial Times (4,218 articles) represent the Western bloc. Xinhua, Global Times and China Daily represent Chinese state-aligned views. State-affiliated Russian and Ukrainian media are also included.
  • Metadata: Each record carries a UUID, URL, normalized source name, ISO country and language codes, and ISO 8601 publication and collection dates.
  • Annotation guidelines:
    • Topical framing is multi-label, with confidence scores between 0 and 1 for each frame.
    • Stance is labeled per sentence toward a target entity as favor, against or none.
    • Thematic tags were produced with GPT-3.5.
  • Sentiment case study (Bucha aftermath, March–April 2022):
    • Russian outlets scored strongly negative as well, which the authors attribute to violent war vocabulary rather than editorial stance.
    • U.S. and U.K. sentiment was volatile.
    • Chinese coverage stayed moderate overall.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/5ecxdux9s9ft

@inproceedings{mohanty2026longitudinal,
  title = {{A Longitudinal, Multinational, and Multilingual Corpus of News Coverage of the Russo-Ukrainian War}},
  author = {Mohanty, Dikshya and Sabadyn, Taisiia and Rodrigues, Jelwin and Wang, Chenlu and Kalugade, Abhishek and Banerjee, Ritwik},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/5ecxdux9s9ft},
  url = {https://doi.org/10.63317/5ecxdux9s9ft}
}

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