Entity-Aware Multi-Perspective Semantic Fusion Network for Fact-Checking Fake News Detection

Fact-checking is a highly challenging task that requires verifying the truthfulness of a claim based on multiple evidence sentences. Despite the effectiveness of existing methods, they overlook the differences in the importance of various news entities. Additionally, they fail to consider the semantic relationships between the claim and the evidence from multiple perspectives. To address these issues, we propose an Entity-aware Multi-perspective Semantic Fusion Network (EMSFN) for Fact-checking Fake News Detection. First, we introduce the Entity Attention Network to extract semantic information from claim and calculate the differences in the importance of entities. Then, we built the Multi-view Semantic Relation Extraction Network to capture the interactions between claim and evidence, extracting multi-dimensional interaction information. The proposed EMSFN calculates the contribution degree of different entities and facilitates information interaction between claim and evidence from multiple perspectives. Experiments on Snopes and PolitiFact datasets validate the effectiveness of our EMSFN.