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

Anonymous

Paper Link: https://openreview.net/forum?id=2bAHv09NM8l

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

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.

Presentation Mode: This paper will be presented virtually

Virtual Presentation Timezone: UTC+8

Copyright Consent Signature (type Name Or NA If Not Transferrable): Wenqian Zhang

Copyright Consent Name And Address: Wenqian Zhang, No.28, Xianning West Road, Xi'an City, Shaanxi Province, PR China, 710049, School of Computer Science and Technology, Xi’an Jiaotong University