  Fact checking is an essential challenge when combating fake news. Identifying
documents that agree or disagree with a particular statement (claim) is a core
task in this process. In this context, stance detection aims at identifying the
position (stance) of a document towards a claim. Most approaches address this
task through a 4-class classification model where the class distribution is
highly imbalanced. Therefore, they are particularly ineffective in detecting
the minority classes (for instance, 'disagree'), even though such instances are
crucial for tasks such as fact-checking by providing evidence for detecting
false claims. In this paper, we exploit the hierarchical nature of stance
classes, which allows us to propose a modular pipeline of cascading binary
classifiers, enabling performance tuning on a per step and class basis. We
implement our approach through a combination of neural and traditional
classification models that highlight the misclassification costs of minority
classes. Evaluation results demonstrate state-of-the-art performance of our
approach and its ability to significantly improve the classification
performance of the important 'disagree' class.
