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