  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.
