  Media outlets are becoming more partisan and polarized nowadays. In this
paper, we identify media bias at the sentence level, and pinpoint bias
sentences that intend to sway readers' opinions. As bias sentences are often
expressed in a neutral and factual way, considering broader context outside a
sentence can help reveal the bias. In particular, we observe that events in a
bias sentence need to be understood in associations with other events in the
document. Therefore, we propose to construct an event relation graph to
explicitly reason about event-event relations for sentence-level bias
identification. The designed event relation graph consists of events as nodes
and four common types of event relations: coreference, temporal, causal, and
subevent relations. Then, we incorporate event relation graph for bias
sentences identification in two steps: an event-aware language model is built
to inject the events and event relations knowledge into the basic language
model via soft labels; further, a relation-aware graph attention network is
designed to update sentence embedding with events and event relations
information based on hard labels. Experiments on two benchmark datasets
demonstrate that our approach with the aid of event relation graph improves
both precision and recall of bias sentence identification.
