Fake news refers to news containing deceptive or fabricated contents that are actually groundless; they are intentionally overstated or provide false information. As such, fake news can distort reality and cause social problems, such as self-misdiagnosis of medical issues. Many academic researchers have been collecting data from social and medical media, which are sources of various information flows, and conducting studies to analyse and detect fake news. However, in the case of conventional studies, the features used for analysis are limited, and the consideration for newly added features of social media is lacking. This project module proposes a fake news analysis modelling method to identify a variety of features in terms of spreading information from Twitter, a social media outlet with a good deal of power in terms of spreading information. Furthermore, fake news was analyzed through neural networkbased classification modelling by using the preprocessed data and the identified best features in the learning data.