In today's society, claims are everywhere, in the online and offline world. Fact-checking models can check these claims and predict if a claim is true or false, but how can these models be checked? Post-hoc XAI feature attribution methods can be used for this. These methods give scores indicating the influence of the individual tokens on the model's decision-making. In our research, we evaluate three popular feature attribution methods in the context of fact-checking: LIME, Kernel SHAP, and Integrated Gradients. We used the NLP architecture ExPred as a fact-checking model in our research. The feature attribution methods were evaluated using a human-grounded and pseudo ground truth evaluation. The results from these evaluations indicate that Integrated Gradients enables humans to form an opinion better and performs better in our pseudo ground truth evaluation. A potential explanation is that the iterations should be increased for LIME and Kernel SHAP. Our findings suggest that Integrated Gradients performs better in our study. Still, more research for other tasks and models would be beneficial to ensure that these results apply to other cases.