  Slanted news coverage, also called media bias, can heavily influence how news
consumers interpret and react to the news. To automatically identify biased
language, we present an exploratory approach that compares the context of
related words. We train two word embedding models, one on texts of left-wing,
the other on right-wing news outlets. Our hypothesis is that a word's
representations in both word embedding spaces are more similar for non-biased
words than biased words. The underlying idea is that the context of biased
words in different news outlets varies more strongly than the one of non-biased
words, since the perception of a word as being biased differs depending on its
context. While we do not find statistical significance to accept the
hypothesis, the results show the effectiveness of the approach. For example,
after a linear mapping of both word embeddings spaces, 31% of the words with
the largest distances potentially induce bias. To improve the results, we find
that the dataset needs to be significantly larger, and we derive further
methodology as future research direction. To our knowledge, this paper presents
the first in-depth look at the context of bias words measured by word
embeddings.
