EPRVFL: A fast and scalable model for real-time fake news detection

Abstract

The widespread dissemination of fake news on social media platforms emphasizes the need for efficient detection methods. In this research, we propose the Embedding Privileged Random Vector Functional Link (EPRVFL) model to improve the real-time detection of fake news while minimizing the inference time. In the proposed EPRVFL model, the text data with diverse datasets, PolitiFact, LIAR2, and BuzzFeed-Webis, were preprocessed and tokenized for analysis to ensure adaptability in various scenarios. The proposed model employs a shallow neural network with a connection between input and output layers to efficiently classify and integrate experimentally finalized bidirectional encoder representations from transformers (BERT) embeddings.

The proposed model achieves inference times of 0.0011 s, 0.0208 s, and 0.0053 s while maintaining high accuracies of 91.7722%, 74.6516%, and 70.3703% on PolitiFact, LIAR2, and BuzzFeed-Webis, respectively. Compared to CNN (1.3395 s, 72.0819%) and BiGRU (0.5518 s, 73.9547%), the EPRVFL ensures significantly faster inference with competitive accuracy. While BiLSTM achieves a higher precision (98.3471%) on PolitiFact, it requires 0.7674 s, making it less efficient in real-time scenarios. Similarly, FFNN shows the fastest inference (0.1103 s) but struggles with accuracy (59.4595%) on BuzzFeed-Webis. The proposed model’s balanced performance across precision, recall and F1 scores reinforces its robustness in fake news detection. The proposed EPRVFL model uniquely integrates BERT-base embeddings with a lightweight neural structure, ensuring rapid inference while maintaining robust accuracy, making it ideal for real-time applications. These findings provide analytical evidence for the model’s applicability in large-scale scenarios and the potential for future research by incorporating enhanced context analysis.