Adaptive Semantic Feature Refinement for Explainable Fake News Detection Using Pretrained Transformers

Abstract

The online media has proliferated and become more accessible, so too has the ease with which misinformation can spread and be consumed and automated mechanisms to detect this form of online deception will be very important research targets moving forward. The implementation of new techniques from deep learning and transformer models pre-trained on large amounts of data has greatly improved the ability to detect misinformation, however many detectors are hindered by limitations on their ability to utilize semantic features and interpret the resulting predictions. In this study, we present our Adaptive Semantic Feature Refinement for Explainable Fake News Detection Utilizing Pre-Trained Transformers model, which uses a novel hybrid deep learning architecture that combines DeBERTa-v3, Bidirectional Gated Recurrent Unit (BiGRU), Multi-Head Self-Attention, and an Adaptive Semantic Refinement Module (ASRM) to produce higher-quality representations of the text used to classify fake news articles into binary categories. In addition, by applying SHAP (SHapley Additive exPlanations) values and Integrated Gradients to improve prediction transparency, we were able to produce both global and local explanations of the model's predictions. Our model was tested using a large corpus of fake news articles that included 682,661 articles, the resulting accuracy was 85.85%, with a ROC AUC statistic of .9294. Overall, results indicate that our proposed architecture successfully combines the process of refining the semantic features of text data while providing an explainable artificial intelligence solution for real-world applications of fake news detection.