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Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection

Lev Konstantinovskiy, Oliver Price, Mevan Babakar, Arkaitz Zubiaga

ACM DTRAP. 2021.

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In an effort to assist factcheckers in the process of factchecking, we tackle the claim detection task, one of the necessary stages prior to determining the veracity of a claim. It consists of identifying the set of sentences, out of a long text, deemed capable of being factchecked. This paper is a collaborative work between Full Fact, an independent factchecking charity, and academic partners. Leveraging the expertise of professional factcheckers, we develop an annotation schema and a benchmark for automated claim detection that is more consistent across time, topics and annotators than previous approaches. Our annotation schema has been used to crowdsource the annotation of a dataset with sentences from UK political TV shows. We introduce an approach based on universal sentence representations to perform the classification, achieving an F1 score of 0.83, with over 5% relative improvement over the state-of-the-art methods ClaimBuster and ClaimRank. The system was deployed in production and received positive user feedback.
    author = {Konstantinovskiy, Lev and Price, Oliver and Babakar, Mevan and Zubiaga, Arkaitz},
    title = {Toward Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection},
    year = {2021},
    issue_date = {April 2021},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    volume = {2},
    number = {2},
    issn = {2692-1626},
    url = {https://doi.org/10.1145/3412869},
    doi = {10.1145/3412869},
    journal = {Digital Threats: Research and Practice},
    month = apr,
    articleno = {14},
    numpages = {16},
    keywords = {debates, classification, factchecking, Claim detection}