A Sentiment-Enhanced Hybrid CNN–GRU–LSTM Framework for Fake News Detection
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Abstract
Online misinformation spreads rapidly across modern digital ecosystems, necessitating automated, high-precision identification systems. While deep learning has advanced textual analysis, conventional single-architecture models frequently overlook the subtle emotional manipulation and complex linguistic patterns typical of deceptive journalism. This multi-tiered hybrid neural framework engineered to detect fake news by combining structural semantic extraction with emotional trajectory tracking.The proposed model utilizes a parallel processing architecture featuring Convolutional Neural Networks (CNNs) alongside recurrent variants, specifically Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) blocks. In this setup, the CNN layers isolate local contextual keyphrases and spatial n-gram patterns, while the integrated LSTM and GRU channels capture sequential narrative dependencies and long-range textual anomalies. By fusing these distinct behavioral and structural feature vectors into a unified embedding space, the model successfully identifies deceptive articles that mimic legitimate writing styles but rely on emotionally manipulative messaging. Empirical evaluations conducted on standard reference repositories validate the efficacy of this methodology. The integrated CNN-GRU-LSTM sentiment model demonstrates superior classification metrics, securing a peak accuracy of 96.2%, which markedly surpasses baseline deep architectures.
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