KGAlign: Joint Semantic-Structural Knowledge Encoding for Multimodal Fake News Detection

TL;DR

KGAlign introduces a multi-modal fake news detection framework that combines visual, textual, and knowledge-based features, utilizing object-level attention, external knowledge graphs, and Transformer classifiers to improve accuracy.

Contribution

It presents a novel knowledge-grounded multimodal reasoning approach that integrates entity-level selection and semantic filtering for enhanced fake news detection.

Findings

Outperforms recent methods in accuracy

Effective use of knowledge graphs for semantic understanding

Improved detection through multi-modal feature fusion

Abstract

Fake news detection remains a challenging problem due to the complex interplay between textual misinformation, manipulated images, and external knowledge reasoning. While existing approaches have achieved notable results in verifying veracity and cross-modal consistency, two key challenges persist: (1) Existing methods often consider only the global image context while neglecting local object-level details, and (2) they fail to incorporate external knowledge and entity relationships for deeper semantic understanding. To address these challenges, we propose a novel multi-modal fake news detection framework that integrates visual, textual, and knowledge-based representations. Our approach leverages bottom-up attention to capture fine-grained object details, CLIP for global image semantics, and RoBERTa for context-aware text encoding. We further enhance knowledge utilization by retrieving and adaptively selecting relevant entities from a knowledge graph. The fused multi-modal features are processed through a Transformer-based classifier to predict news veracity. Experimental results demonstrate that our model outperforms recent approaches, showcasing the effectiveness of neighbor selection mechanism and multi-modal fusion for fake news detection. Our proposal introduces a new paradigm: knowledge-grounded multimodal reasoning. By integrating explicit entity-level selection and NLI-guided filtering, we shift fake news detection from feature fusion to semantically grounded verification. For reproducibility and further research, the source code is publicly at \href{https://github.com/latuanvinh1998/KGAlign}{github.com/latuanvinh1998/KGAlign}.