Towards Fine-Grained Reasoning for Fake News Detection

• URL: http://arxiv.org/abs/2110.15064v4
• Date: Sat, 5 Mar 2022 06:44:10 GMT
• Title: Towards Fine-Grained Reasoning for Fake News Detection
• Authors: Yiqiao Jin, Xiting Wang, Ruichao Yang, Yizhou Sun, Wei Wang, Hao Liao, Xing Xie
• Abstract summary: We move towards fine-grained reasoning for fake news detection by better reflecting the logical processes of human thinking. In particular, we propose a fine-grained reasoning framework by following the human information-processing model.
• Score: 43.497126436856426
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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