StyleGraph: A Heterogeneous Graph Neural Framework for Stylistic and Semantic Rumor Detection on Social Media - Haider Jaffar; Ali Mohades; Mohammad Ebrahim Shiri (IEEE Access)
Identifying and controlling misinformation circulated on social media is known as rumor detection. The growing prevalence of deceptive content presents a significant challenge that necessitates advanced detection techniques, beyond classical machine learning. This study introduced StyleGraph, a novel graph-based framework that enhances rumor detection by jointly leveraging semantic and stylistic information. Unlike previous methods that focused solely on textual content or propagation structures, Style-Graph constructs a heterogeneous graph that models interactions among authors, phrases, and comments. We integrate stylistic features derived from author-writing patterns with semantic representations to enrich node embeddings. StyleGraph was evaluated using Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and Cluster GCNs on the PHEME dataset under both 5-event and 9-event settings. The Cluster GCN model achieved the highest accuracy of 62.7% and an F1-score of 55.5% on the 9-event dataset, while the GAT model achieved 77.7% accuracy on the 5-event dataset. Further improvements were observed with the use of data augmentation and sentence-BERT (SBERT) embeddings, with GAT achieving an F1-score of 75.3%. The proposed framework demonstrates scalability and effectiveness in real-time rumor detection scenarios.