Towards Fine-Grained Reasoning for Fake News Detection


  • Yiqiao Jin University of California, Los Angeles
  • Xiting Wang Microsoft Research Asia
  • Ruichao Yang Hong Kong Baptist University
  • Yizhou Sun University of California, Los Angeles
  • Wei Wang University of California, Los Angeles
  • Hao Liao Shenzhen University
  • Xing Xie Microsoft Research Asia



Knowledge Representation And Reasoning (KRR), Speech & Natural Language Processing (SNLP), Data Mining & Knowledge Management (DMKM), Machine Learning (ML)


The detection of fake news often requires sophisticated reasoning skills, such as logically combining information by considering word-level subtle clues. In this paper, we move towards fine-grained reasoning for fake news detection by better reflecting the logical processes of human thinking and enabling the modeling of subtle clues. In particular, we propose a fine-grained reasoning framework by following the human’s information-processing model, introduce a mutual-reinforcement-based method for incorporating human knowledge about which evidence is more important, and design a prior-aware bi-channel kernel graph network to model subtle differences between pieces of evidence. Extensive experiments show that our model outperforms the state-of-the-art methods and demonstrate the explainability of our approach.




How to Cite

Jin, Y., Wang, X., Yang, R., Sun, Y., Wang, W., Liao, H., & Xie, X. (2022). Towards Fine-Grained Reasoning for Fake News Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 36(5), 5746-5754.



AAAI Technical Track on Knowledge Representation and Reasoning