Wenqian Zhang

Wenqian Zhang, Shangbin Feng, Zilong Chen et al.

Political perspective detection has become an increasingly important task that can help combat echo chambers and political polarization. Previous approaches generally focus on leveraging textual content to identify stances, while they fail to reason with background knowledge or leverage the rich semantic and syntactic textual labels in news articles. In light of these limitations, we propose KCD, a political perspective detection approach to enable multi-hop knowledge reasoning and incorporate textual cues as paragraph-level labels. Specifically, we firstly generate random walks on external knowledge graphs and infuse them with news text representations. We then construct a heterogeneous information network to jointly model news content as well as semantic, syntactic and entity cues in news articles. Finally, we adopt relational graph neural networks for graph-level representation learning and conduct political perspective detection. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods on two benchmark datasets. We further examine the effect of knowledge walks and textual cues and how they contribute to our approach's data efficiency.

Shangbin Feng, Zilong Chen, Wenqian Zhang et al.

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.