A study of retrospective and on-line event detection
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
On-line new event detection and tracking
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
WebMaster: knowledge-based verification of Web-pages
IEA/AIE '99 Proceedings of the 12th international conference on Industrial and engineering applications of artificial intelligence and expert systems: multiple approaches to intelligent systems
A comparative web browser (CWB) for browsing and comparing web pages
WWW '03 Proceedings of the 12th international conference on World Wide Web
Newsjunkie: providing personalized newsfeeds via analysis of information novelty
Proceedings of the 13th international conference on World Wide Web
A statistical model for domain-independent text segmentation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Tracking and summarizing news on a daily basis with Columbia's Newsblaster
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Analysis of News Agencies' Descriptive Features of People and Organizations
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
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News content is one kind of popular and valuable information on the Web. Since news agencies have different viewpoints and collect different news materials, their perspectives on news contents may be diverse (biased). In such cases, it is important to indicate this bias and diversity to newsreaders. In this paper, we propose a system called TVBanc (Topic and Viewpoint based Bias Analysis of News Content) to analyze diversity and bias in Web-news content based on comparisons of topics and viewpoints. The topic and viewpoint of a news item are represented by using a novel notion called a content structure consisting touple of subject, aspect and state terms. Given a news item, TVBanc facilitates bias analysis in three steps: first, TVBanc extracts the topic and viewpoint of that news item based on its content structure. Second, TVBanc searches for related news items from multi-sources such as TV-news programs, video news clips, and articles on the Web. Finally, TVBanc groups the related news items into different clusters, and analyzes their distribution to estimate the diversity and bias of the news contents. The details of clustering results are also presented to help users understand the different viewpoints of the news contents. This paper also presents some experimental results we obtained to validate the methods we propose.