Multilingual Previously Fact-Checked Claim Retrieval

Fact-checkers are often hampered by the sheer amount of online content that needs to be fact-checked. NLP can help them by retrieving already existing fact-checks relevant to the content being investigated. This paper introduces a new multilingual dataset for previously fact-checked claim retrieval. We collected 28k posts in 27 languages from social media, 206k fact-checks in 39 languages written by professional fact-checkers, as well as 31k connections between these two groups. This is the most extensive and the most linguistically diverse dataset of this kind to date. We evaluated how different unsupervised methods fare on this dataset and its various dimensions. We show that evaluating such a diverse dataset has its complexities and proper care needs to be taken before interpreting the results. We also evaluated a supervised fine-tuning approach, improving upon the unsupervised method significantly.

Authors

Ivan Srba
Researcher
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Róbert Móro
Researcher
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Jakub Šimko
Lead and Researcher
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Maria Bielikova
Lead and Researcher
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Matúš Pikuliak
Research Consultant
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Juraj Podroužek
Lead and Researcher
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Martin Melišek
Research Intern
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Timotej Smoleň
Research Intern 06/2022-04/2023
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Timo Hromádka
Research Intern 07/2022-01/2023
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Ivan Vykopal
PhD Student
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