Enhancing Social Media Rumor Detection: A Semantic and Graph Neural Network Approach for the 2024 Global Election

2025-03-03

AI Summary

During the 2024 global elections, rapid dissemination of political misinformation and extremist discourse on social media posed critical threats to public cognition and electoral integrity. To address this, we propose a fine-grained rumor detection framework integrating semantic understanding with propagation-structure modeling. Our approach introduces SAGEWithEdgeAttention—a novel graph neural network that employs BERT-based fine-tuned text encoders for semantic representation, constructs directed heterogeneous social propagation graphs, and—uniquely—incorporates first-order temporal differences as edge attributes. Coupled with an edge-attention mechanism, this jointly captures both rumor evolutionary dynamics and user relational topology. Extensive experiments on PolitiFact and Twitter political rumor datasets demonstrate that our method achieves over 8.2% improvement in accuracy and F1-score compared to conventional content-based and sequential baselines. It further exhibits strong generalizability and suitability for real-time deployment.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

The development of social media platforms has revolutionized the speed and manner in which information is disseminated, leading to both beneficial and detrimental effects on society. While these platforms facilitate rapid communication, they also accelerate the spread of rumors and extremist speech, impacting public perception and behavior significantly. This issue is particularly pronounced during election periods, where the influence of social media on election outcomes has become a matter of global concern. With the unprecedented number of elections in 2024, against this backdrop, the election ecosystem has encountered unprecedented challenges. This study addresses the urgent need for effective rumor detection on social media by proposing a novel method that combines semantic analysis with graph neural networks. We have meticulously collected a dataset from PolitiFact and Twitter, focusing on politically relevant rumors. Our approach involves semantic analysis using a fine-tuned BERT model to vectorize text content and construct a directed graph where tweets and comments are nodes, and interactions are edges. The core of our method is a graph neural network, SAGEWithEdgeAttention, which extends the GraphSAGE model by incorporating first-order differences as edge attributes and applying an attention mechanism to enhance feature aggregation. This innovative approach allows for the fine-grained analysis of the complex social network structure, improving rumor detection accuracy. The study concludes that our method significantly outperforms traditional content analysis and time-based models, offering a theoretically sound and practically efficient solution.

Problem

• Detect social media rumors during 2024 global elections
• Combine semantic analysis with graph neural networks
• Improve accuracy in identifying politically relevant rumors

Innovation

• Semantic analysis with fine-tuned BERT model
• Graph neural network SAGEWithEdgeAttention
• Directed graph with tweets and comments as nodes