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Award ID contains: 2346261

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  1. ; ; ; (, Association for Computing Machinery)
    Free, publicly-accessible full text available November 10, 2026
  2. ; ; ; (, Association for Computing Machinery)
    Free, publicly-accessible full text available November 10, 2026
  3. ; ; ; (, Association for Computational Linguistics)
    Free, publicly-accessible full text available July 27, 2026
  4. ; ; ; (, Association for Computational Linguistics)
    Free, publicly-accessible full text available July 27, 2026
  5. ; ; ; (, Springer Nature Switzerland)
    Free, publicly-accessible full text available January 24, 2026
  6. ; ; ; ; ; ; (, ACM Transactions on Intelligent Systems and Technology)
    This article presents the latest developments to ClaimBuster’s claim-spotting model, which tackles the critical task of identifying check-worthy claims from large streams of information. We introduce the first adversarially regularized, transformer-based claim-spotting model, which achieves state-of-the-art results on several benchmark datasets. In addition to analyzing model performance metrics, we also quantitatively and qualitatively analyze the impact of ClaimBuster’s real-world deployment. Moreover, to help facilitate reproducibility and community engagement, we publicly release our codebase, dataset, data curation platform, API, Google Colab notebooks, and various ClaimBuster-based demo systems, atclaimbuster.org. 
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    Free, publicly-accessible full text available December 31, 2025
  7. ; ; ; ; (, Association for Computational Linguistics)
    Full Text Available