  We introduce a classification scheme for detecting political bias in long
text content such as newspaper opinion articles. Obtaining long text data and
annotations at sufficient scale for training is difficult, but it is relatively
easy to extract political polarity from tweets through their authorship. We
train on tweets and perform inference on articles. Universal sentence encoders
and other existing methods that aim to address this domain-adaptation scenario
deliver inaccurate and inconsistent predictions on articles, which we show is
due to a difference in opinion concentration between tweets and articles. We
propose a two-step classification scheme that uses a neutral detector trained
on tweets to remove neutral sentences from articles in order to align opinion
concentration and therefore improve accuracy on that domain. Our implementation
is available for public use at https://knowbias.ml.
