Aspect-level news browsing: understanding news events from multiple viewpoints

Aspect-level news browsing provides readers with a classified view of news articles with different viewpoints. It facilitates active interactions with which readers easily discover and compare diverse existing biased views over a news event. As such, it effectively helps readers understand the event from a plural of viewpoints and formulate their own, more balanced viewpoints free from specific biased views. Realizing aspect-level browsing raises important challenges, mainly due to the lack of semantic knowledge with which to abstract and classify the intended salient aspects of articles. We first demonstrate the feasibility of aspect-level news browsing through user studies. We then deeply look into the news article production process and develop framing cycle-aware clustering. The evaluation results show that the developed method performs classification more accurately than other methods.

[1]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[2]  August E. Grant,et al.  The Spiral of Opportunity and Frame Resonance: Mapping the Issue Cycle in News and Public Discourse , 2001 .

[3]  Seungwoo Kang,et al.  Mitigating media bias: a computational approach , 2008, WebScience '08.

[4]  Dean S. Karlan,et al.  Does the Media Matter? A Field Experiment Measuring the Effect of Newspapers on Voting Behavior and Political Opinions , 2006 .

[5]  Jiahui Liu,et al.  Rich interfaces for reading news on the web , 2009, IUI.

[6]  Yiming Yang,et al.  Topic Detection and Tracking Pilot Study Final Report , 1998 .

[7]  Patricia A. Curtin.  Reevaluating Public Relations Information Subsidies: Market-Driven Journalism and Agenda-Building Theory and Practice , 1999 .

[8]  George Karypis,et al.  A Comparison of Document Clustering Techniques , 2000 .

[9]  Dragomir R. Radev,et al.  NewsInEssence: summarizing online news topics , 2005, Commun. ACM.

[10]  Seungwoo Kang,et al.  NewsCube: delivering multiple aspects of news to mitigate media bias , 2009, CHI.

[11]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[12]  C. Sunstein.  Republic.com , 2001 .

[13]  Xiaojun Wan,et al.  CollabSum: exploiting multiple document clustering for collaborative single document summarizations , 2007, SIGIR.

[14]  Mattias Polborn,et al.  Political Polarization and the Electoral Effects of Media Bias , 2006, SSRN Electronic Journal.

[15]  Justus J. Randolph.  Free-Marginal Multirater Kappa (multirater K[free]): An Alternative to Fleiss' Fixed-Marginal Multirater Kappa. , 2005 .

[16]  Robert M. Entman,et al.  Framing: Toward Clarification of a Fractured Paradigm , 1993 .

[17]  June Woong Rhee,et al.  Framing the health care reform campaign of 1993-1994: News frame, interpretation, and public opinion change , 1997 .

[18]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[19]  Steven Kull,et al.  Misperceptions, the Media, and the Iraq War , 2003 .