Global-Local Ensemble Detector for AI-Generated Fake News
Year: 2025
Type: article
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 face difficulties in accurately detecting such content, due to their limited capacity to differentiate between authentic and fabricated news. To address this issue, this paper introduces a novel "Global-Local News Detection Model", which combines BERT, Bidirectional Long Short-Term Memory (BiLSTM) networks, Text Convolutional Neural Networks (TextCNN), and attention mechanisms to enhance the detection of AI-generated fake news. A new dataset, generated using GPT-4 and covering 42 news categories, was developed to serve as a comprehensive and diverse foundation for training and evaluating the model. Experimental results indicate that the proposed model achieves an accuracy and F1 score of 0.82, surpassing traditional approaches.
Source: IEEE Access
Authors: Yujia Wang, Wen Long
Institution: Guizhou University of Finance and Economics
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,075
Awards: G1866184523, G3186093012
OpenAlex ID: W4409581184