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

Wenqian Zhang, Shangbin Feng, Zilong Chen, Zhenyu Lei, Jundong Li, Minnan Luo


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.
Anthology ID:
2022.naacl-main.304
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4129–4140
Language:
URL:
https://aclanthology.org/2022.naacl-main.304
DOI:
10.18653/v1/2022.naacl-main.304
Bibkey:
Cite (ACL):
Wenqian Zhang, Shangbin Feng, Zilong Chen, Zhenyu Lei, Jundong Li, and Minnan Luo. 2022. KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4129–4140, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media (Zhang et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.304.pdf
Video:
 https://aclanthology.org/2022.naacl-main.304.mp4
Code
 wenqian-zhang/kcd