HEX-Net:Ensemble for Fake News Detection

The unchecked diffusion of fabricated and misleading news across digital platforms has created a need for detection systems that are accurate, interpretable, and deployable at scale. This paper proposes HEF-XFND, a hybrid explainable feature-fusion framework that combines sparse lexical evidence, contextual transformer representations, source-level credibility indicators, and calibrated ensemble learning. The architecture extracts term frequency-inverse document frequency features, linguistic style descriptors, and BERT/RoBERTa sentence embeddings. These heterogeneous representations are projected into a common feature space and processed by complementary classifiers, including logistic regression, linear support vector machines, random forests, XGBoost, and a lightweight neural classifier. A stacking layer produces the final veracity probability, while SHAP- and LIME-based explanations identify the words and feature groups that most strongly influence a decision. The framework also includes probability calibration, confidence scoring, crossvalidation, and latency-aware model selection for high-volume deployment. A reproducible experimental protocol is presented for the Fake and Real News dataset containing 44,898 articles. Literature-informed illustrative results indicate that transformer-assisted ensemble fusion can outperform isolated conventional and neural baselines while retaining practical inference efficiency. The proposed design addresses three recurring limitations in fake-news research: dependence on a single representation, insufficient explanation of predictions, and inadequate consideration of operational scalability. The manuscript provides a transparent foundation for subsequent implementation, external validation, and deployment in newsrooms, fact-checking services, and social-media moderation systems.

Fake news detection, natural language processing, transformer embeddings, ensemble learning, explainable artificial intelligence, TF-IDF, RoBERTa, scalability.

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