  Media plays an important role in shaping public opinion. Biased media can
influence people in undesirable directions and hence should be unmasked as
such. We observe that featurebased and neural text classification approaches
which rely only on the distribution of low-level lexical information fail to
detect media bias. This weakness becomes most noticeable for articles on new
events, where words appear in new contexts and hence their "bias
predictiveness" is unclear. In this paper, we therefore study how second-order
information about biased statements in an article helps to improve detection
effectiveness. In particular, we utilize the probability distributions of the
frequency, positions, and sequential order of lexical and informational
sentence-level bias in a Gaussian Mixture Model. On an existing media bias
dataset, we find that the frequency and positions of biased statements strongly
impact article-level bias, whereas their exact sequential order is secondary.
Using a standard model for sentence-level bias detection, we provide empirical
evidence that article-level bias detectors that use second-order information
clearly outperform those without.
