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- W4225485802 abstract "With the proliferation of fake news in the last few years, especially during the COVID-19 period, combating the spread of misinformation has become an urgent need. Although automated fact-checking systems were proposed recently, they leave much to be desired in terms of accuracy and explainability. Therefore, involving humans during verification could make the process much easier and more reliable. In this work, we propose an automated approach to detect claims that have been already manually-verified by professional fact-checkers. Our proposed approach uses recent powerful BERT variants as point-wise rerankers. Additionally, we study the impact of using different fields of the verified claim during training and inference phases. Experimental results show that our proposed pipeline outperforms the state-of-the-art approaches on two English and one Arabic datasets." @default.
- W4225485802 created "2022-05-05" @default.
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- W4225485802 date "2022-01-01" @default.
- W4225485802 modified "2023-10-18" @default.
- W4225485802 title "Did I See It Before? Detecting Previously-Checked Claims over Twitter" @default.
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- W4225485802 doi "https://doi.org/10.1007/978-3-030-99736-6_25" @default.
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