A Study of ResNet-Enhanced CNN Versus State-of-the-Art Models for Fake News Detection

Fake news leads to dire consequences at both the individual and community levels. It has the potential to ruin reputations, incite communal riots, alter election outcomes etc. Convolutional Neural Networks (CNNs) are effective for fake news detection, but stacking many layers can limit their ability to learn high-level feature representations. Residual Networks (ResNets) allows the network to learn incremental refinements over earlier features, leading to deeper, more discriminative representations that capture the subtle patterns critical for distinguishing fake news. This study investigates the integration of ResNets into CNN models to enhance their performance in detecting fake news. Various CNN-ResNet combinations are evaluated on three publicly available datasets: MM-COVID, PubHealth, and Covid19FakeNews. The effectiveness of these models is assessed and compared with state-of-the-art methods. The ResNet-enhanced CNN model demonstrates promising results in fake news detection, though further optimization is needed for certain datasets. Future work should explore more advanced residual architectures, fine-tuning of pre-trained embeddings, and hyperparameter optimization while testing on larger, more diverse datasets to confirm real-world applicability.