Fake news detection through multi-level contextual and sequential modeling
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Abstract
The spread of false information on digital platforms destabilizes democratic societies and undermines public trust. Conventional machine-learning models often fail to capture the subtle contextual cues and long‑range linguistic dependencies required for accurate verification. To address these limitations, we propose a hybrid deep‑learning architecture that integrates three modules: A Contextual Encoder Module (CEM) using RoBERTa to generate relational embeddings for deep semantic extraction; a Bidirectional Sequence Learner (BSL) to model long‑range temporal dependencies; and a Context Refinement Layer (CRL) that employs attention mechanisms to highlight the most salient deceptive markers. We evaluated the model on the WELFake dataset, which contains 72,134 news articles. Our proposed method achieved 99% accuracy, precision, recall, and F1‑score, significantly outperforming state‑of‑the‑art baselines, including BERT (95%) and BiLSTM (97%). The CRL's attention outputs provide transparency into the model's decision‑making process, an essential feature for applications in misinformation detection and automated content moderation. Beyond its technical contributions, this research supports society by enabling early and reliable detection of false information, reducing harm from misinformation, and promoting a safer, more trustworthy digital environment.
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