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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.more » « lessFree, publicly-accessible full text available December 31, 2025
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Arslan, Fatma; Hassan, Naeemul; Li, Chengkai; Tremayne, Mark (, Proceedings of the Fourteenth International AAAI Conference on Web and Social Media (ICWSM 2020))
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Arslan, Fatma; Caraballo, Josue; Jimenez, Damian; Li, Chengkai (, Proceedings of The 12th Language Resources and Evaluation Conference)
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Zhu, Zhengyuan; Meng, Kevin; Caraballo, Josue; Jaradat, Israa; Shi, Xiao; Zhang, Zeyu; Akrami, Farahnaz; Liao, Haojin; Arslan, Fatma; Jimenez, Damian; et al (, Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations)
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Arslan, Fatma; Jimenez, Damian; Caraballo, Josue; Zhang, Gensheng; Li, Chengkai. (, Proceedings of the Computation + Journalism Symposium)
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Majithia, Sarthak; Arslan, Fatma; Lubal, Sumeet; Jimenez, Damian; Arora, Priyank; Caraballo, Josue; Li, Chengkai. (, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations)
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