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
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Extracting significant time varying features from text
Proceedings of the eighth international conference on Information and knowledge management
Automatic generation of overview timelines
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Combining semantic and syntactic document classifiers to improve first story detection
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A System for new event detection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Text classification and named entities for new event detection
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Query based event extraction along a timeline
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Parameter free bursty events detection in text streams
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Hot Topic Extraction Based on Timeline Analysis and Multidimensional Sentence Modeling
IEEE Transactions on Knowledge and Data Engineering
Analyzing feature trajectories for event detection
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
New event detection based on indexing-tree and named entity
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Time-dependent event hierarchy construction
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic online news issue construction in web environment
Proceedings of the 17th international conference on World Wide Web
Using Burstiness to Improve Clustering of Topics in News Streams
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Semi-automatic hot event detection
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
An Automatic Online News Topic Keyphrase Extraction System
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Topic-Based Computing Model for Web Page Popularity and Website Influence
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Hot Topic Detection on BBS Using Aging Theory
WISM '09 Proceedings of the International Conference on Web Information Systems and Mining
Emerging topic detection on Twitter based on temporal and social terms evaluation
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Mining the blogosphere for top news stories identification
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Evaluating importance of websites on news topics
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Chinese new word detection from query logs
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
ImpactWheel: Visual Analysis of the Impact of Online News
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Social event radar: a bilingual context mining and sentiment analysis summarization system
ACL '12 Proceedings of the ACL 2012 System Demonstrations
Processing continuous text queries featuring non-homogeneous scoring functions
Proceedings of the 21st ACM international conference on Information and knowledge management
Ranking news events by influence decay and information fusion for media and users
Proceedings of the 21st ACM international conference on Information and knowledge management
Personalized emerging topic detection based on a term aging model
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Grey System Theory based prediction for topic trend on Internet
Engineering Applications of Artificial Intelligence
Hi-index | 0.01 |
News topics, which are constructed from news stories using the techniques of Topic Detection and Tracking (TDT), bring convenience to users who intend to see what is going on through the Internet. However, it is almost impossible to view all the generated topics, because of the large amount. So it will be helpful if all topics are ranked and the top ones, which are both timely and important, can be viewed with high priority. Generally, topic ranking is determined by two primary factors. One is how frequently and recently a topic is reported by the media; the other is how much attention users pay to it. Both media focus and user attention varies as time goes on, so the effect of time on topic ranking has already been included. However, inconsistency exists between both factors. In this paper, an automatic online news topic ranking algorithm is proposed based on inconsistency analysis between media focus and user attention. News stories are organized into topics, which are ranked in terms of both media focus and user attention. Experiments performed on practical Web datasets show that the topic ranking result reflects the influence of time, the media and users. The main contributions of this paper are as follows. First, we present the quantitative measure of the inconsistency between media focus and user attention, which provides a basis for topic ranking and an experimental evidence to show that there is a gap between what the media provide and what users view. Second, to the best of our knowledge, it is the first attempt to synthesize the two factors into one algorithm for automatic online topic ranking.