Document Type

Article

Source of Publication

Journal of Physics: Complexity

Publication Date

6-1-2025

Abstract

In the modern era of digital technology, the rapid distribution of news via social media platforms substantially contributes to the propagation of false information, presenting challenges in upholding the accuracy and reliability of information. This study presents an updated approach that utilizes graph neural networks (GNNs) alongside with advanced deep learning techniques to improve the identification of false information. In contrast to traditional approaches that primarily rely on analyzing text and assessing the credibility of sources, our methodology utilizes the structural information of news propagation networks. This allows for a detailed comprehension of the interconnections and patterns that are indicative of misinformation. By analyzing the intricate, graph-based connections between news items, our approach not only overcomes the constraints of conventional fake news detection methods but also demonstrates significant enhancements in detection accuracy. This paper emphasizes the revolutionary nature of utilizing GNNs in the field of fake news detection. It also examines the potential consequences of our research in reducing the propagation of false information. Our model achieved an impressive accuracy rate of 97%, demonstrating a significant improvement in its ability to identify and classify fake news.The findings highlight the substantial improvement in the ability to detect fake news provided by GNNs in comparison to traditional methods, demonstrating promising growth in the struggle against false information.

ISSN

2632-072X

Publisher

IOP Publishing

Volume

6

Issue

2

Disciplines

Computer Sciences

Keywords

deep learning, fake news detection, graph neural network, natural language processing

Scopus ID

05001795701

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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