MultiClaimNet: A Massively Multilingual Dataset of Fact-Checked Claim Clusters

MultiClaimNet is a collection of three multilingual claim cluster datasets. The claims discussing similar facts are automatically grouped and annotated with a cluster ID. The following three datasets within MultiClaimNet contain claims written in 86 languages across diverse topics. 

Preprint: https://arxiv.org/abs/2503.22280

Content:

• Claim - Factchecked Claim
• ClusterID - Cluster ID
• Language - Original language of the claim
• Translation - English translation
• NID - Unique identifier of the Claim

In addition to the above fields, the MultiClaim dataset contains the following fields from the original dataset.

• Timestamp
• URL

References

If you use any dataset from MultiClaimNet, in any publication, project, tool, or in any other form, please, cite the following paper:

@misc{panchendrarajan2025multiclaimnet,
      title={MultiClaimNet: A Massively Multilingual Dataset of Fact-Checked Claim Clusters}, 
      author={Rrubaa Panchendrarajan and Rubén Míguez and Arkaitz Zubiaga},
      year={2025},
      eprint={2503.22280},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.22280}, 
}

If you use the MultiClaim dataset from the MultiClaimNet collection in any publication, project, tool, or in any other form, please, cite the following paper in addition to the above:

@inproceedings{pikuliak-etal-2023-multilingual,
    title = "Multilingual Previously Fact-Checked Claim Retrieval",
    author = "Pikuliak, Mat{\'u}{\v{s}} and Srba, Ivan and Moro, Robert and Hromadka, Timo and Smole{\v{n}}, Timotej and Meli{\v{s}}ek, Martin and Vykopal, Ivan and Simko, Jakub and Podrou{\v{z}}ek, Juraj and Bielikova, Maria",
    editor = "Bouamor, Houda  and Pino, Juan  and Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.1027",
    doi = "10.18653/v1/2023.emnlp-main.1027",
    pages = "16477--16500",
}