Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Information diffusion through blogspace
ACM SIGKDD Explorations Newsletter
Visualization of News Distribution in Blog Space
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
ACM Transactions on Information Systems (TOIS)
Evaluation over thousands of queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Using a model of social dynamics to predict popularity of news
Proceedings of the 19th international conference on World wide web
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Mining the blogosphere for top news stories identification
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
ACM SIGIR Forum
Domain-specific identification of topics and trends in the blogosphere
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
News article ranking: leveraging the wisdom of bloggers
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
| Hi-index | 0.00 |
Nowadays, people receive information of the news stories not only from newspapers but also from online news websites. They search important news stories in order to know what happen today. However, it is hard to browse all the news stories published on a day. It is necessary to identify which news stories are more newsworthy on the specific day. In this paper, we investigate how to automatically identify the importance of news stories for different news categories on a specific day by utilizing the influence propagation among communities and news categories. In particular, we build an influence propagation model which consists of three features: category relevance, bloggers' attention and bursty influence. Based on this influence propagation model, we propose a Cross-Category Social Influence Propagation (C-SIP) approach for scoring the importance of news stories on a specific day. We evaluate our approach by using the judgment of Story Ranking Task in TREC 2010 Blog Track. The experiment shows our approach attains a prominent performance in the retrieval of important news stories and gets 9.94% improvement over the best performance of participating systems in TREC 2010 Blog Track.