I-FLASH: Interpretable Fake News Detector Using LIME and SHAP

This paper proposes I-FLASH, an interpretable fake news detector that not only detects fake news but also explains why it considers some content fake or genuine, and compared the performance of the machine learning model (logistic regression with TF-IDF), deep learning model, and the pre-trained Bidirectional Encoder Representations from Transformers model on curated datasets along with two other popular datasets, namely, LIAR and COVID-19.