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

Chang Li     Dan Goldwasser    
Association for Computational Linguistics (ACL), 2019
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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


Bib Entry

  @InProceedings{LiG_acl_2019,
    author = "Chang Li and Dan Goldwasser",
    title = "Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Media",
    booktitle = "Association for Computational Linguistics (ACL)",
    year = "2019"
  }