Contrasting Opposing Views of News Articles on Contentious Issues

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 340–349, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Contrasting Opposing Views of News Articles on Contentious Issues Souneil Park1, KyungSoon Lee2, Junehwa Song1 1Korea Advanced Institute of Science and Technology 2Chonbuk National University 291 Daehak-ro, Yuseong-gu, 664-14 1ga Deokjin-dong Jeonju, Daejeon, Republic of Korea Jeonbuk, Republic of Korea {spark,junesong}@nclab.kaist.ac.kr selfsolee@chonbuk.ac.kr Abstract We present disputant relation-based meth- od for classifying news articles on conten- tious issues. We observe that the disputants of a contention are an important feature for understanding the discourse. It performs unsupervised classification on news articles based on disputant relations, and helps readers intuitively view the articles through the opponent-based frame. The readers can attain balanced understanding on the con- tention, free from a specific biased view. We applied a modified version of HITS al- gorithm and an SVM classifier trained with pseudo-relevant data for article analysis. 1 Introduction The coverage of contentious issues of a community is an essential function of journalism. Contentious issues continuously arise in various domains, such as politics, economy, environment; each issue in- volves diverse participants and their different com- plex arguments. However, news articles are frequently biased and fail to fairly deliver conflict- ing arguments of the issue. It is difficult for ordi- nary readers to analyze the conflicting arguments and understand the contention; they mostly per- ceive the issue passively, often through a single article. Advanced news delivery models are re- quired to increase awareness on conflicting views. In this paper, we present disputant relation- based method for classifying news articles on con- tentious issues. We observe that the disputants of a contention, i.e., people who take a position and participate in the contention such as politicians, companies, stakeholders, civic groups, experts, commentators, etc., are an important feature for understanding the discourse. News producers pri- marily shape an article on a contention by selecting and covering specific disputants (Baker. 1994). Readers also intuitively understand the contention by identifying who the opposing disputants are. The method helps readers intuitively view the news articles through the opponent-based frame. It performs classification in an unsupervised manner: it dynamically identifies opposing disputant groups and classifies the articles according to their posi- tions. As such, it effectively helps readers contrast articles of a contention and attain balanced under- standing, free from specific biased viewpoints. The proposed method differs from those used in related tasks as it aims to perform classification under the opponent-based frame. Research on sen- timent classification and debate stance recognition takes a topic-oriented view, and attempts to per- form classification under the „positive vs. negative‟ or „for vs. against‟ frame for the given topic, e.g., positive vs. negative about iPhone. However, such frames are often not appropriate for classifying news articles of a contention. The coverage of a contention often spans over different topics (Miller. 2001). For the contention on the health care bill, an article may discuss the enlarged coverage whereas another may discuss the increase …

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