  Identifying political perspectives in news media has become an important task
due to the rapid growth of political commentary and the increasingly polarized
political ideologies. Previous approaches focus on textual content and leave
out the rich social and political context that is essential in the perspective
detection process. To address this limitation, we propose KGAP, a political
perspective detection method that incorporates external domain knowledge.
Specifically, we construct a political knowledge graph to serve as
domain-specific external knowledge. We then construct heterogeneous information
networks to represent news documents, which jointly model news text and
external knowledge. Finally, we adopt relational graph neural networks and
conduct political perspective detection as graph-level classification.
Extensive experiments demonstrate that our method consistently achieves the
best performance on two real-world perspective detection benchmarks. Ablation
studies further bear out the necessity of external knowledge and the
effectiveness of our graph-based approach.
