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This paper introduces a theory-informed computational framework for automated fake news detection and explanation, integrating cross-disciplinary insights from social sciences and psychology, and validates it through experiments on benchmark datasets.
The paper audits the widely used ISOT/Kaggle fake news corpus, revealing that high accuracy in text classifiers stems from shortcut learning via metadata and style signals rather than genuine veracity assessment.
This paper explores Mamba-based State Space Models for Bangla fake news detection, comparing them to Transformer models like BanglaBERT. It demonstrates that Mamba offers competitive performance with higher efficiency in resource-constrained settings.
This paper proposes Expert-Guided Mutual Distillation (EGMD) to address domain bias and semantic misalignment in multimodal fake news detection, achieving state-of-the-art accuracy and reducing domain bias by up to 57.3% across four datasets.
Introduces KITE, a tri-modal transformer framework that jointly models text, images, and knowledge graphs for fake news detection, outperforming unimodal and bimodal baselines on benchmark datasets.
Proposes FIND-IT!, a multimodal fake news detection framework for Indian news using ResNet-50 for visual features, DistilBERT for text, and ANFIS with attention fusion to classify news as fake or real.