Advancing fake news detection with Graph Neural Network and Deep Learning

• Main Author: Gul, Haji
• Publication Date: 2024
• Other Authors: Al-Obeidat, Feras, Wasim, Muhammad, Amin, Adnan, Moreira, Fernando
• Format: Article
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: https://hdl.handle.net/11328/6000
• topic: Natural Language Processing (NLP); Fake News Detection; Text Classification; Deep Learning; Graph Neural Network; Machine Learning; Text Complexity Monitoring; Ciências Naturais - Ciências da Computação e da Informação

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 Graph Neural Networks (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.