Explainable Stacked Ensemble Learning for Fake News Detection using Text and Metadata

Abstract

Purpose: Automated fake news detection can support verification, but predictions are less useful when textual and contextual evidence cannot be inspected. The study examined whether combining claim text with speaker metadata could improve veracity classification while retaining explanations for journalistic review.   Methods: The LIAR train, validation, and test partitions were used, with six veracity labels remapped into Fake and Real classes. A BERT-base text classifier and Extreme Gradient Boosting metadata classifier produced class probabilities combined by an L1-regularized logistic regression meta-learner with disagreement and interaction features. SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations, and Integrated Gradients inspected metadata, ensemble, and token-level behavior.   Findings: The stacking model reached 0.7514 accuracy, 0.7483 macro-F1, 0.8288 area under the receiver operating characteristic curve, and 0.4970 Matthews correlation coefficient. It exceeded the BERT text branch (macro-F1=0.6203) and metadata branch (macro-F1=0.7066). Metadata provided the stronger signal, with credibility score and speaker-history variables receiving the largest SHAP importance. High text-metadata disagreement occurred in 30.4% of test cases, and the ensemble reached 0.8234 accuracy within this subset.   Originality: The study extends explainable fake-news detection by conceptualizing disagreement between evidence sources as a dimension of interpretability, separating semantic claim evidence, contextual speaker-history evidence, and their interaction at the fusion stage.