  The spread of fake news has long been a social issue and the necessity of
identifying it has become evident since its dangers are well recognized. In
addition to causing uneasiness among the public, it has even more devastating
consequences. For instance, it might lead to death during pandemics due to
unverified medical instructions. This study aims to build a model for
identifying fake news using graphs and machine learning algorithms. Instead of
scanning the news content or user information, the research explicitly focuses
on the spreading network, which shows the interconnection among people, and
graph features such as the Eigenvector centrality, Jaccard Coefficient, and the
shortest path. Fourteen features are extracted from graphs and tested in
thirteen machine learning models. After analyzing these features and comparing
the test result of machine learning models, the results reflect that propensity
and centrality contribute highly to the classification. The best performing
models reach 0.9913 and 0.9987 separately from datasets Twitter15 and Twitter16
using a modified tree classifier and Support Vector Classifier. This model can
effectively predict fake news, prevent potential negative social impact caused
by fake news, and provide a new perspective on graph feature selection for
machine learning models.
