The Digester

Hca-fnd is a hybrid two-tier approach for fake news detection that fuses NLP content analysis and machine learning decision layers, reporting stronger performance and better interpretability than single-stage models while noting limits from data bias and adversarial risks.

Hca-fnd uses a two-tier architecture with a content analysis tier that extracts linguistic and transformer-based embeddings and a decision tier that fuses those signals with contextual features for final classification.
The model combines classic features such as TF-IDF and named entity recognition with transformer embeddings to capture both stylistic cues and semantic meaning.
An ensemble of machine learning classifiers and fine-tuned neural encoders is used to balance robustness and computational cost.
Authors report improved detection performance on standard fake news benchmarks compared with single-tier baselines while avoiding reliance on any single model class.
Explainability methods such as SHAP are applied to surface the most influential features for human reviewers and aid interpretability.
Key limitations include dataset bias, vulnerability to adversarial or multimodal manipulation, and the ongoing need for human-in-the-loop verification and domain adaptation.

Hca-fnd: Hybrid two-tier fake news detector that pairs NLP with machine learning

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