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.

通过研究基于高斯混合模型的概率分布，发现频率和位置对于文章层面的媒体偏见检测至关重要，其次是其精确的时序，相较于只依赖于低水平词汇信息分布的特征和神经文本分类方法。