  We present the Newspaper Bias Dataset (NewB), a text corpus of more than
200,000 sentences from eleven news sources regarding Donald Trump. While
previous datasets have labeled sentences as either liberal or conservative,
NewB covers the political views of eleven popular media sources, capturing more
nuanced political viewpoints than a traditional binary classification system
does. We train two state-of-the-art deep learning models to predict the news
source of a given sentence from eleven newspapers and find that a recurrent
neural network achieved top-1, top-3, and top-5 accuracies of 33.3%, 61.4%, and
77.6%, respectively, significantly outperforming a baseline logistic regression
model's accuracies of 18.3%, 42.6%, and 60.8%. Using the news source label of
sentences, we analyze the top n-grams with our model to gain meaningful insight
into the portrayal of Trump by media sources.We hope that the public release of
our dataset will encourage further research in using natural language
processing to analyze more complex political biases.
  Our dataset is posted at https://github.com/JerryWeiAI/NewB .
