@inproceedings{jin2021towards,
author = {Jin, Yiqiao and Wang, Xiting and Yang, Ruichao and Sun, Yizhou and Wang, Wei and Liao, Hao and Xie, Xing},
title = {Towards Fine-Grained Reasoning for Fake News Detection},
booktitle = {AAAI 2022},
year = {2021},
month = {December},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/towards-fine-grained-reasoning-for-fake-news-detection/},
}