TEGRA: Text Encoding With Graph and Retrieval Augmentation for Misinformation Detection
This work addresses misinformation detection, a critical problem for fact-checking and information integrity, but it is incremental as it builds on existing methods with domain-specific enhancements.
The authors tackled misinformation detection by proposing TEGRA, a method that encodes text with graph and retrieval augmentation, enhancing classification performance compared to using language models alone.
Misinformation detection is a critical task that can benefit significantly from the integration of external knowledge, much like manual fact-checking. In this work, we propose a novel method for representing textual documents that facilitates the incorporation of information from a knowledge base. Our approach, Text Encoding with Graph (TEG), processes documents by extracting structured information in the form of a graph and encoding both the text and the graph for classification purposes. Through extensive experiments, we demonstrate that this hybrid representation enhances misinformation detection performance compared to using language models alone. Furthermore, we introduce TEGRA, an extension of our framework that integrates domain-specific knowledge, further enhancing classification accuracy in most cases.