KEGAT: A Knowledge-Enhanced Graph-Aware Transformer for Detecting AI-Generated Fake News

Abstract

With the continuous evolution of advanced large language models like GPT, the proliferation of AI-generated fake news presents growing challenges to information dissemination. Traditional text classification methods struggle to detect such content due to their limited capacity to distinguish between authentic and fabricated news. To address this issue, this study introduces an MLP (Multi-Layer Perceptron) Classifier integrated with Natural Language Processing (NLP) techniques for detecting AI-generated fake news. Textual data is preprocessed through tokenization, stop-word removal, and vectorization to extract meaningful features, which are then used as inputs to the MLP network. The classifier leverages multiple hidden layers and nonlinear activation functions to capture complex linguistic patterns that characterize fabricated news. A new dataset, generated using GPT-4 and covering 42 news categories, was developed to train and evaluate the system. Experimental results demonstrate that the proposed MLP model achieves reliable accuracy and strong F1 scores, surpassing traditional machine learning approaches. These findings highlight the potential of MLP-based architectures in enhancing fake news detection and safeguarding online information integrity.