  We present experiments on detecting hyperpartisanship in news using a
'masking' method that allows us to assess the role of style vs. content for the
task at hand. Our results corroborate previous research on this task in that
topic related features yield better results than stylistic ones. We
additionally show that competitive results can be achieved by simply including
higher-length n-grams, which suggests the need to develop more challenging
datasets and tasks that address implicit and more subtle forms of bias.
