Authors:
Zhixuan Zhou
1
;
Huankang Guan
2
;
Meghana Moorthy Bhat
3
and
Justin Hsu
3
Affiliations:
1
Hongyi Honor College, Wuhan University, Wuhan, China, Department of Computer Science, University of Wisconsin-Madison, Madison and U.S.A.
;
2
Hongyi Honor College, Wuhan University, Wuhan and China
;
3
Department of Computer Science, University of Wisconsin-Madison, Madison and U.S.A.
Keyword(s):
Fake News Detection, NLP, Attack, Fact Checking, Outsourced Knowledge Graph.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Natural Language Processing
;
Pattern Recognition
;
Symbolic Systems
Abstract:
News plays a significant role in shaping people’s beliefs and opinions. Fake news has always been a problem, which wasn’t exposed to the mass public until the past election cycle for the 45th President of the United States. While quite a few detection methods have been proposed to combat fake news since 2015, they focus mainly on linguistic aspects of an article without any fact checking. In this paper, we argue that these models have the potential to misclassify fact-tampering fake news as well as under-written real news. Through experiments on Fakebox, a state-of-the-art fake news detector, we show that fact tampering attacks can be effective. To address these weaknesses, we argue that fact checking should be adopted in conjunction with linguistic characteristics analysis, so as to truly separate fake news from real news. A crowdsourced knowledge graph is proposed as a straw man solution to collecting timely facts about news events.