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

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2594–2604 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 2594 Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Media Chang Li Purdue University li1873@purdue.edu Dan Goldwasser Purdue University dgoldwas@purdue.edu Abstract Identifying the political perspective shaping the way news events are discussed in the me- dia is an important and challenging task. In this paper, we highlight the importance of con- textualizing social information, capturing how this information is disseminated in social net- works. We use Graph Convolutional Net- works, a recently proposed neural architecture for representing relational information, to cap- ture 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 so- cial information can significantly improve per- formance. 1 Introduction Over the last decade we witness a dramatic change in the way information is generated and dissem- inated. Instead of a few dedicated sources that employ reporters and fact checkers to ensure the validity of the information they provide, social platforms now provide the means for any user to distribute their content, resulting in a sharp in- crease in the number of information outlets and articles covering news events. As a direct result of this process, the information provided is often shaped by their underlying perspectives, interests and ideologies. For example, consider the follow- ing two snippets discussing the comments made by a Democratic Senator regarding the recent U.S. government shutdown. thehill.com (Center) Sen. Mark Warner (D-Va.) on Sunday blasted Pres- ident Trump for his “inept negotiation” to bring an end to the ongoing partial government shutdown. Warner, the ranking member of the Senate Intelli- gence Committee, lamented the effect the shutdown has had on hundreds of thousands of federal workers who have been furloughed or forced to work without pay. infowars.com (Right) Senator Mark Warner (D-Va.) is being called out on social media for his statement on the partial govern- ment shutdown. Warner blamed the “suffering” of federal workers and contractors on President Trump in a Sunday tweet framing Trump as an “inept ne- gotiator”. Twitter users pointed out that Democrats are attending a Puerto Rican retreat with over 100 lobbyists and corporate executives. Despite the fact that both articles discuss the same event, they take very different perspectives. The first reporting directly about the comments made, while the second one focuses on negative reactions to these comments. Identifying the per- spective difference and making it explicit can help strengthen trust in the newly-formed information landscape and ensure that all perspectives are rep- resented. It can also help lay the foundation for the automatic detection of false content and ru- mors and help identify information echo-chambers in which only a single perspective is highlighted. Traditionally, identifying the author’s perspec- tive is studied as a text-categorization prob- lem (Greene and Resnik, 2009; Beigman Kle- banov et al., 2010; Recasens …

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