The field of explainable Automatic Fact-Checking (AFC) aims to enhance the transparency and trustworthiness of automated fact verification systems by providing clear and comprehensible explanations. However, the effectiveness of these explanations depends ontheir actionability—the extent to which an AFC explanation pinpoints the error, supplies the correct fact, and backs it with sources. Despiteactionability being critical for high-quality explanations, no prior research has proposed a method to evaluate it. This paper introducesFinGrAct, a fine-grained evaluation framework that can access the web and is designed to assess actionability in AFC explanations through well-defined criteria. We also introduce a novel dataset to evaluate actionability in AFC explanations. FinGrAct surpasses state-of-the-art (SOTA) evaluators, achieving the highest Pearson and Kendall correlation with human judgments while demonstrating the lowest egocentricbias, making it a more robust evaluation approach for actionability evaluation in AFC.

Automated Fact-Checking (AFC) is the automated verification of claim accuracy. AFC is crucial in discerning truth from misinformation, especially given the huge amounts of content are generated online daily. Current research focuses on predicting claim veracity through metadata analysis and language scrutiny, with an emphasis on justifying verdicts. This paper surveys recent methodologies, proposinga comprehensive taxonomy and presenting the evolution of research in that landscape. A comparative analysis of methodologies and futuredirections for improving fact-checking explainability are also discussed.