Supervisor
Maqsood Hussain
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
The increasing influence of social media has significantly impacted how news spreads among users, making the detection of misinformation, including fake news and rumours, a critical task. Previous research has explored content-based and metadata-driven approaches, while recent advancements have leveraged Graph Neural Networks (GNNs) such as GCNs, GATs, and SAGE to analyse user interactions and propagation patterns. This thesis investigates the effectiveness of a Hybrid Graph-Transformer Convolutional Neural Network (GCN-Transformer) for fake news detection, combining graph-based learning with Transformer attention mechanisms. Through in-depth data analysis and advanced detection techniques, this model aims to enhance predictive performance and mitigate the spread of misinformation.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Zuin Gigli, L.
(2025) Enhancing Fake News and Rumor Detection Using Metadata, Context, and User Interaction with a Hybrid GCN-Transformer. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.94