  The increasing prevalence of political bias in news media calls for greater
public awareness of it, as well as robust methods for its detection. While
prior work in NLP has primarily focused on the lexical bias captured by
linguistic attributes such as word choice and syntax, other types of bias stem
from the actual content selected for inclusion in the text. In this work, we
investigate the effects of informational bias: factual content that can
nevertheless be deployed to sway reader opinion. We first produce a new
dataset, BASIL, of 300 news articles annotated with 1,727 bias spans and find
evidence that informational bias appears in news articles more frequently than
lexical bias. We further study our annotations to observe how informational
bias surfaces in news articles by different media outlets. Lastly, a baseline
model for informational bias prediction is presented by fine-tuning BERT on our
labeled data, indicating the challenges of the task and future directions.
