  Stance detection, the task of identifying the speaker's opinion towards a
particular target, has attracted the attention of researchers. This paper
describes a novel approach for detecting stance in Twitter. We define a set of
features in order to consider the context surrounding a target of interest with
the final aim of training a model for predicting the stance towards the
mentioned targets. In particular, we are interested in investigating political
debates in social media. For this reason we evaluated our approach focusing on
two targets of the SemEval-2016 Task6 on Detecting stance in tweets, which are
related to the political campaign for the 2016 U.S. presidential elections:
Hillary Clinton vs. Donald Trump. For the sake of comparison with the state of
the art, we evaluated our model against the dataset released in the
SemEval-2016 Task 6 shared task competition. Our results outperform the best
ones obtained by participating teams, and show that information about enemies
and friends of politicians help in detecting stance towards them.
