  Identifying the stance of a news article body with respect to a certain
headline is the first step to automated fake news detection. In this paper, we
introduce a 2-stage ensemble model to solve the stance detection task. By using
only hand-crafted features as input to a gradient boosting classifier, we are
able to achieve a score of 9161.5 out of 11651.25 (78.63%) on the official Fake
News Challenge (Stage 1) dataset. We identify the most useful features for
detecting fake news and discuss how sampling techniques can be used to improve
recall accuracy on a highly imbalanced dataset.
