Research on Fake News Detection Based on Dual Evidence Perception

Abstract

With the development of online social networks, the prevalence of fake news on social media is increasing, leading to a heightened focus on detecting fake news. To combat this, researchers have developed various strategies, including text-based and fact-checking approaches. The text-based approach analyses linguistic features within fake news posts, while the fact-checking method verifies statements against an external knowledge base, disregarding the linguistic patterns between posts. Despite their differences, both approaches offer complementary benefits in the fight against fake news, highlighting the importance of integrating these methods. However, there has been relatively limited investigation into merging these two strategies into a unified framework. In this paper, we proposed the FakE News Detection Based on Dual Evidence Perception (DEP-FEND) method. This novel approach merges text-based and fact-checking techniques into a unified framework to enhance fake news detection. DEP-FEND consists of three sub-modules: evidence perception based on the historical news environment, evidence perception from an external knowledge base, and the fake news detector. Initially, two independent modules were developed to gather evidence from the historical news environment and an external knowledge base using the Gaussian kernel soft-counting method, generating two distinct evidence vectors. Subsequently, these two independent perception modules were combined using domain gates, and the posts, along with the combined evidence perception signals, were inputted into the fake news detector. The experimental results on the two Chinese datasets demonstrate that our proposed solution outperformed other fake news detection methods with an average accuracy improvement of 3.6% and a macro-f1 improvement of 2.1%.