Improving Recommender Systems for Fake News Detection in Social Networks with Knowledge Graphs and Graph Attention Networks

Aleksei Golovin (), Nataly Zhukova, Radhakrishnan Delhibabu () and Alexey Subbotin
Additional contact information
Aleksei Golovin: Department of Software Engineering and Computer Applications, Saint Petersburg Electrotechnical University ‘LETI’, 197376 St. Petersburg, Russia
Nataly Zhukova: St. Petersburg Federal Research Centre of the Russian Academy of Sciences (SPCRAS), 199178 St. Petersburg, Russia
Radhakrishnan Delhibabu: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
Alexey Subbotin: Department of Software Engineering and Computer Applications, Saint Petersburg Electrotechnical University ‘LETI’, 197376 St. Petersburg, Russia

Mathematics, 2025, vol. 13, issue 6, 1-16

Abstract: This paper addresses the pervasive problem of fake news propagation in social networks. Traditional text-based detection models often suffer from performance degradation over time due to their reliance on evolving textual features. To overcome this limitation, we propose a novel recommender system that leverages the power of knowledge graphs and graph attention networks (GATs). This approach captures both the semantic relationships within the news content and the underlying social network structure, enabling more accurate and robust fake news detection. The GAT model, by assigning different weights to neighboring nodes, effectively captures the importance of various users in disseminating information. We conducted a comprehensive evaluation of our system using the FakeNewsNet dataset, comparing its performance against classical machine learning models and the DistilBERT language model. Our results demonstrate that the proposed graph-based system achieves state-of-the-art performance, with an F1-score of 95%, significantly outperforming other models. Moreover, it maintains its effectiveness over time, unlike text-based approaches that are susceptible to concept drift. This research underscores the potential of knowledge graphs and GATs in combating fake news and provides a robust framework for building more resilient and accurate detection systems.

Keywords: fake news detection; social networks; knowledge graphs; graph attention networks; recommender system; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/6/1011/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/6/1011/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:6:p:1011-:d:1616659

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().