Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
First story detection in TDT is hard
Proceedings of the ninth international conference on Information and knowledge management
The web as a graph: measurements, models, and methods
COCOON'99 Proceedings of the 5th annual international conference on Computing and combinatorics
Predicting News Story Importance Using Language Features
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Learning the importance of latent topics to discover highly influential news items
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Progress in information retrieval
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
| Hi-index | 0.00 |
Reading news is one of the most popular activities when people surf the internet. As too many news sources provide independent news information and each has its own preference, detecting unbiased important news might be very useful for users to keep up to date with what are happening in the world. In this paper we present a novel method to identify important news in web environment which consists of diversified online news sites. We observe that a piece of important news generally occupies visually significant place in some homepage of a news site and import news event will be reported by many news sites. To explore these two properties, we model the relationship between homepages, news and latent events by a tripartite graph, and present an algorithm to identify important news in this model. Based on this algorithm, we implement a system TOPSTORY to dynamically generate homepages for users to browse important news reports. Our experimental study indicates the effectiveness of proposed approach.