A New cross-domain strategy based XAI models for fake news detection
This addresses fake news detection for social media and information systems, but it appears incremental as it combines existing methods.
The paper tackles fake news detection by developing a four-level cross-domain strategy using pre-trained BERT models, achieving an ideal pairing of explainable AI (XAI) models like Anchor and SHAP across different domain levels.
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.