Towards Fine-Grained Reasoning for Fake News Detection

The proposed FinerFact framework


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 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.

In Proceedings of the 36th AAAI Conference (AAAI'22)

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Yiqiao Jin
Yiqiao Jin
Graduate Research Assistant at Georgia Institute of Technology

My research interests include Computational Social Science, Misinformation, Graph Analysis, and Data Mining.