KGAP: Knowledge Graph Augmented Political Perspective Detection in News Media
This addresses the challenge of political perspective detection for media analysts and researchers, offering an incremental improvement by integrating external knowledge into existing methods.
The paper tackled the problem of detecting political perspectives in news media by incorporating external domain knowledge through a political knowledge graph and heterogeneous information networks, achieving the best performance on two real-world benchmarks.
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.