Towards Fine-Grained Reasoning for Fake News Detection

Jan 1, 2022·
Yiqiao Jin
Yiqiao Jin
,
Xiting Wang
,
Ruichao Yang
,
Yizhou Sun
,
Wei Wang
,
Hao Liao
,
Xing Xie
· 1 min read
Figure showing the main model architecture and workflow Our FinerFact model.
Abstract
We propose a fine-grained reasoning framework for fake news detection by following the human information-processing model and designing a prior-aware bi-channel kernel graph network.
Type
Publication
AAAI Conference on Artificial Intelligence 2022

Abstract

We propose a fine-grained reasoning framework for fake news detection by following the human information-processing model and designing a prior-aware bi-channel kernel graph network.

Keywords

Fake News Detection, Graph Neural Networks, Reasoning, Misinformation

Fake News Detection Graph Neural Networks Reasoning Misinformation
Yiqiao Jin
Authors
Yiqiao Jin
Ph.D. Candidate in Computer Science
My research sits at the intersection of multimodal foundation models, intelligent agents, and spatial/physical intelligence. I build general-purpose models that perceive, reason, learn, and act in interactive virtual and physical environments.