ArticleScientific data2026
Multilingual news dataset about Ukraine (2022-2025): data collection and documentation.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Multilingual news dataset about Ukraine (2022-2025): data collection and documentation.Scientific data · 2026Article
- Bridging jurisdictions and legislatures: an LLM ensemble and arbiter framework for automated legal risk triage in digital media.Frontiers in artificial intelligence · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This Data Descriptor introduces a multilingual news dataset about Ukraine spanning 2022-2025. It contains 120,617 articles gathered from publicly available online news sources and organized to support research on the information environment surrounding the war. The dataset includes article metadata and text fields, as well as thematic labels that help researchers study how key issues are discussed over time and across outlets. It is intended to support research on media coverage and information environments, supporting research on misleading narratives, and analysing trends in media coverage. The collection can also be used to develop and evaluate natural language processing methods for text classification, topic analysis, and comparative studies of reporting across languages. By providing a large, structured corpus focused on a high-impact geopolitical context, the dataset enables reproducible experiments and offers a practical foundation for researchers and practitioners interested in media analysis, disinformation studies, and information resilience.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.