Disputant Relation-Based Classification for Contrasting Opposing Views of Contentious News Issues

Contentious news issues, such as the health care reform debate, draw much interest from the public; however, it is not simple for an ordinary user to search and contrast the opposing arguments and have a comprehensive understanding of the issues. Providing a classified view of the opposing views of the issues can help readers easily understand the issue from multiple perspectives. We present a disputant relation-based method for classifying news articles on contentious 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 and attain balanced understanding, free from a specific biased viewpoint. The method is performed in three stages: disputant extraction, disputant partitioning, and article classification. We apply a modified version of HITS algorithm and an SVM classifier trained with pseudorelevant data for article analysis. We conduct an accuracy analysis and an upper-bound analysis for the evaluation of the method.

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 involves diverse participants and their different complex arguments. However, news articles are frequently biased and fail to fairly deliver conflicting arguments of the issue. It is difficult for ordinary readers to analyze the conflicting arguments and understand the contention; they mostly perceive the issue passively, often through a single article. Advanced news delivery models are required to increase awareness on conflicting views.