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 2022, Oral Presentation

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
Final-year 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.