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.

Online debates can help provide valuable information about various perspectives on a wide range of issues. However, understanding the stances expressed in these debates is a highly challenging task, which requires modeling both textual content and users’ conversational interactions. Current approaches take a collective classification approach, which ignores the relationships between different debate topics. In this work, we suggest to view this task as a representation learning problem, and embed the text and authors jointly based on their interactions. We evaluate our model over the Internet Argumentation Corpus, and compare different approaches for structural information embedding. Experimental results show that our model can achieve significantly better results compared to previous competitive models.