  Informational bias is widely present in news articles. It refers to providing
one-sided, selective or suggestive information of specific aspects of certain
entity to guide a specific interpretation, thereby biasing the reader's
opinion. Sentence-level informational bias detection is a very challenging task
in a way that such bias can only be revealed together with the context,
examples include collecting information from various sources or analyzing the
entire article in combination with the background. In this paper, we integrate
three levels of context to detect the sentence-level informational bias in
English news articles: adjacent sentences, whole article, and articles from
other news outlets describing the same event. Our model, MultiCTX (Multi-level
ConTeXt), uses contrastive learning and sentence graphs together with Graph
Attention Network (GAT) to encode these three degrees of context at different
stages by tactically composing contrastive triplets and constructing sentence
graphs within events. Our experiments proved that contrastive learning together
with sentence graphs effectively incorporates context in varying degrees and
significantly outperforms the current SOTA model sentence-wise in informational
bias detection.
