Enhancing Fake News Detection by Incorporating Evidence Credibility

Yike Wu, Mengying Liu, Mingming Liu, Jiahao Sun, Yang Xiao, Mengting Hu

Abstract: The evidence-aware fake news detection aims to determine the veracity of claims under the guidance of external evidences. However, existing methods often neglect the credibility of evidences, making them vulnerable to misinformation in real-world scenarios where the evidence credibility is not always guaranteed. In this paper, we incorporate evidence credibility into fake news detection and propose a novel framework named ECFEND, which explicitly models the varying credibility of different evidences. Moreover, we present a new benchmark, SnopesCG, designed to simulate more realistic and challenging scenarios. Each claim in the benchmark is associated with noisy evidences retrieved from web pages as well as generated interference ones. Experimental results demonstrate the superiority of ECFEND over state-of-the-art methods, particularly on SnopesCG. We have open-sourced the code at: https://github.com/nffxdhd88/ECFEND.

External IDs:dblp:conf/ijcnn/WuLLSXH25