  Social media platforms are vulnerable to fake news dissemination, which
causes negative consequences such as panic and wrong medication in the
healthcare domain. Therefore, it is important to automatically detect fake news
in an early stage before they get widely spread. This paper analyzes the impact
of incorporating content information, prior knowledge, and credibility of
sources into models for the early detection of fake news. We propose a
framework modeling those features by using BERT language model and external
sources, namely Simple English Wikipedia and source reliability tags. The
conducted experiments on CONSTRAINT datasets demonstrated the benefit of
integrating these features for the early detection of fake news in the
healthcare domain.
