  Recently, due to the booming influence of online social networks, detecting
fake news is drawing significant attention from both academic communities and
general public. In this paper, we consider the existence of confounding
variables in the features of fake news and use Propensity Score Matching (PSM)
to select generalizable features in order to reduce the effects of the
confounding variables. Experimental results show that the generalizability of
fake news method is significantly better by using PSM than using raw frequency
to select features. We investigate multiple types of fake news methods
(classifiers) such as logistic regression, random forests, and support vector
machines. We have consistent observations of performance improvement.
