  With the rise of phenomena like `fake news' and the growth of heavily-biased
media ecosystems, there has been increased attention on understanding and
evaluating media bias. Of particular note in the evaluation of media bias is
writing style bias, which includes lexical bias and framing bias. We propose a
novel approach to evaluating writing style bias that utilizes natural language
similarity estimation and a network-based representation of the shared content
between articles to perform bias characterization. Our proposed method presents
a new means of evaluating writing style bias that does not rely on human
experts or knowledge of a media producer's publication procedures. The results
of experimentation on real-world vaccine mandate data demonstrates the utility
of the technique and how the standard bias labeling procedures of only having
one bias label for a media producer is insufficient to truly characterize the
bias of that media producer.
