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We propose a novel training and inference method for detecting political biasin long text content such as newspaper opinion articles. Obtaining long textdata and annotations at sufficient scale for training is difficult, but it isrelatively easy to extract political polarity from tweets through theirauthorship; as such, we train on tweets and perform inference on articles.Universal sentence encoders and other existing methods that aim to address thisdomain-adaptation scenario deliver inaccurate and inconsistent predictions onarticles, which we show is due to a difference in opinion concentration betweentweets and articles. We propose a two-step classification scheme that utilizesa neutral detector trained on tweets to remove neutral sentences from articlesin order to align opinion concentration and therefore improve accuracy on thatdomain.  We evaluate our two-step approach using a variety of test suites, including aset of tweets and long-form articles where annotations were crowd-sourced todecrease label noise, measuring accuracy and Spearman-rho rank correlation. Inpractice, KnowBias achieves a high accuracy of 86 (rho = 0.65) on these tweetsand 75 (rho = 0.69) on long-form articles. While we validate our method onpolitical bias, our scheme is general and can be readily applied to othersettings, where there exist such domain mismatches between source and targetdomains. Our implementation is available for public use at https://knowbias.ml.

KnowBias: A Novel AI Method to Detect Polarity in Online Content