Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media

Chang Li, Dan Goldwasser


Abstract
Identifying the political perspective shaping the way news events are discussed in the media is an important and challenging task. In this paper, we highlight the importance of contextualizing social information, capturing how this information is disseminated in social networks. We use Graph Convolutional Networks, a recently proposed neural architecture for representing relational information, to capture the documents’ social context. We show that social information can be used effectively as a source of distant supervision, and when direct supervision is available, even little social information can significantly improve performance.
Anthology ID:
P19-1247
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2594–2604
Language:
URL:
https://www.aclweb.org/anthology/P19-1247
DOI:
10.18653/v1/P19-1247
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1247.pdf