Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection
This addresses fake news detection for social media and information systems, but it is incremental as it builds on existing label propagation methods with LLM integration.
The paper tackles multimodal fake news detection by integrating LLM-generated pseudo labels with global label propagation, achieving superior performance over state-of-the-art baselines on benchmark datasets.
Large Language Models (LLMs) can assist multimodal fake news detection by predicting pseudo labels. However, LLM-generated pseudo labels alone demonstrate poor performance compared to traditional detection methods, making their effective integration non-trivial. In this paper, we propose Global Label Propagation Network with LLM-based Pseudo Labeling (GLPN-LLM) for multimodal fake news detection, which integrates LLM capabilities via label propagation techniques. The global label propagation can utilize LLM-generated pseudo labels, enhancing prediction accuracy by propagating label information among all samples. For label propagation, a mask-based mechanism is designed to prevent label leakage during training by ensuring that training nodes do not propagate their own labels back to themselves. Experimental results on benchmark datasets show that by synergizing LLMs with label propagation, our model achieves superior performance over state-of-the-art baselines.