BEAR-Fact: A Twitter dataset with fact-checking labels, evidence texts and entity and relation annotations
Type
Corpus
Author
Amelie Wührl, Yarik Menchaca Resendiz, Lara Grimminger und Roman Klinger
Description
A dataset of tweets annotated with fact-checking labels, evidence texts and structured knowledge, i.e., biomedical entities and relations.
The annotations in this dataset are licensed under a CC BY-SA license.
Reference
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BioClaim: Biomedical Claims in Tweets
- Type
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Corpus
- Author
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Amelie Wührl, Roman Klinger
- Description
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A corpus of 1200 Twitter posts with annotations of explicit and implicit biomedical claims.
The annotations in this dataset are licensed under a CC BY-SA license.
- Reference
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Wührl, A., & Klinger, R. (2021). Claim Detection in Biomedical Twitter Posts. BioNLP: Proceedings of the 2021 Workshop on Biomedical Natural Language Processing. [paper]
Wührl, A., & Klinger, R. (2021). Claim Detection in Biomedical Twitter Posts as a Prerequisite for Fact-Checking [Poster presentation]. BioCreative VII Workshop. [poster] [abstract]
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BEAR: Biomedical Entities and Relations in Tweets
- Type
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Corpus
- Author
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Amelie Wührl, Roman Klinger
- Description
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A dataset of 2100 Twitter posts annotated with 14 different types of biomedical entities (e.g., disease, treatment, risk factor, etc.) and 20 relation types (including caused, treated, worsens, etc.).
The annotations in this dataset are licensed under a CC BY-SA license.
- Reference
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Wührl, A., & Klinger, R. (2022). Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR). Proceedings of The 13th Language Resources and Evaluation Conference. [paper]
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CoVERT: A Corpus of Crowdsourced Fact-checking Verdicts for Biomedical COVID-19 Tweets
- Type
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Corpus
- Authors
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Isabelle Mohr, Amelie Wührl, Roman Klinger
- Description
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A corpus of 300 Twitter posts with claims about Covid-19. All tweets are annotated with crowdsourced fact-checking verdicts (supports, refutes, not enough info) and evidence texts supporting the verdicts.
The annotations in this dataset are licensed under a CC BY-SA license.
- Reference
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Mohr, I. & Wührl, A. & Klinger, R. (2022). CoVERT: A Corpus of Crowdsourced Fact-checking Verdicts for Biomedical
COVID-19 Tweets. Proceedings of The 13th Language Resources and Evaluation Conference. [paper] - Download
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Corpus Mohr Wührl Klinger CoVERT 2022
(the code repository is/will be at https://github.com/violenil/factCheckCovidTweets)
Amelie Wührl
Roman Klinger
Prof. Dr.Adjunct Professor