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Recently, Fake News Classification (FNC) has evolved to incorporate justifications provided by fact-checkers to explain their decisions. In this work, we argue that an argumentative representation of fact-checkers\u2019 justifications can improve the precision and explainability of FNC systems. To address this challenging task, we present LIARArg, a novel linguistic resource composed of 2,832 news and their justifications. LIARArg extends the 6-label FNC dataset LIAR-PLUS with argumentation structures, leading to the first FNC dataset annotated with argument components (claim and premise) and fine-grained relations (attack, support, partial support and partial attack). To integrate argumentation in FNC, we propose a novel joint learning method combining, for the first time, Argument Mining and FNC which outperforms state-of-the-art approaches, especially for news with intermediate truthfulness labels. Besides, our experimental setting demonstrates that fine-grained relations allow an extra performance boost. We also show that the argumentative representation of human justifications can be exploited in a Chain-of-Thought manner both in prompts and model output, paving a promising avenue for research in explainable fact-checking. Finally, our fully automated pipeline shows that integrating argumentation into FNC is not only feasible but also effective.<\/jats:p>","DOI":"10.1177\/19462174251330980","type":"journal-article","created":{"date-parts":[[2025,4,19]],"date-time":"2025-04-19T06:44:08Z","timestamp":1745045048000},"page":"405-424","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["When automated fact-checking meets argumentation: Unveiling fake news through argumentative evidence"],"prefix":"10.1177","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8849-7725","authenticated-orcid":false,"given":"Xiaoou","family":"Wang","sequence":"first","affiliation":[{"name":"Universit\u00e9 C\u00f4te d\u2019Azur, CNRS, Inria, Nice, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9374-7872","authenticated-orcid":false,"given":"Elena","family":"Cabrio","sequence":"additional","affiliation":[{"name":"Universit\u00e9 C\u00f4te d\u2019Azur, CNRS, Inria, Nice, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3495-493X","authenticated-orcid":false,"given":"Serena","family":"Villata","sequence":"additional","affiliation":[{"name":"Universit\u00e9 C\u00f4te d\u2019Azur, CNRS, Inria, Nice, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,4,22]]},"reference":[{"key":"e_1_3_3_2_2","volume-title":"A multi-dimensional approach to disinformation: report of the independent high level group on fake news and online disinformation","author":"de Cock Buning M","year":"2018","unstructured":"de Cock Buning M. 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