  Expert systems have been used to enable computers to make recommendations and
decisions. This paper presents the use of a machine learning trained expert
system (MLES) for phishing site detection and fake news detection. Both topics
share a similar goal: to design a rule-fact network that allows a computer to
make explainable decisions like domain experts in each respective area. The
phishing website detection study uses a MLES to detect potential phishing
websites by analyzing site properties (like URL length and expiration time).
The fake news detection study uses a MLES rule-fact network to gauge news story
truthfulness based on factors such as emotion, the speaker's political
affiliation status, and job. The two studies use different MLES network
implementations, which are presented and compared herein. The fake news study
utilized a more linear design while the phishing project utilized a more
complex connection structure. Both networks' inputs are based on commonly
available data sets.
