TEGRA: Text Encoding With Graph and Retrieval Augmentation for Misinformation Detection
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915793988485120 |
|---|---|
| author | Faye, Géraud Ouerdane, Wassila Gadek, Guillaume Gatepaille, Sylvain Hudelot, Céline |
| author_facet | Faye, Géraud Ouerdane, Wassila Gadek, Guillaume Gatepaille, Sylvain Hudelot, Céline |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11106 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TEGRA: Text Encoding With Graph and Retrieval Augmentation for Misinformation Detection Faye, Géraud Ouerdane, Wassila Gadek, Guillaume Gatepaille, Sylvain Hudelot, Céline Computation and Language 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. |
| title | TEGRA: Text Encoding With Graph and Retrieval Augmentation for Misinformation Detection |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2602.11106 |