  In this study, we presented a four-level cross-domain strategy for fake news
detection on pre-trained models. Cross-domain text classification is a task of
a model adopting a target domain by using the knowledge of the source domain.
Explainability is crucial in understanding the behaviour of these complex
models. A fine-tune BERT model is used to. perform cross-domain classification
with several experiments using datasets from different domains. Explanatory
models like Anchor, ELI5, LIME and SHAP are used to design a novel explainable
approach to cross-domain levels. The experimental analysis has given an ideal
pair of XAI models on different levels of cross-domain.
