KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media

TL;DR

This paper introduces KCD, a novel method that combines multi-hop knowledge reasoning and textual cues to improve political perspective detection in news media, outperforming existing methods.

Contribution

The paper proposes KCD, integrating knowledge walks and textual cues with graph neural networks for enhanced political perspective detection.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Demonstrates the effectiveness of knowledge walks and textual cues

Shows improved data efficiency with the proposed approach

Abstract

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.