Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Social Information Processing in News Aggregation
IEEE Internet Computing
Identifying the influential bloggers in a community
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Statistical analysis of the social network and discussion threads in slashdot
Proceedings of the 17th international conference on World Wide Web
Analysis of social voting patterns on digg
Proceedings of the first workshop on Online social networks
Digging Digg: Comment Mining, Popularity Prediction, and Social Network Analysis
WISM '09 Proceedings of the 2009 International Conference on Web Information Systems and Mining
Reduced data similarity-based matching for time series patterns alignment
Pattern Recognition Letters
Predicting the popularity of online content
Communications of the ACM
Blog Popularity Mining Using Social Interconnection Analysis
IEEE Internet Computing
Online Social Network Popularity Evolution: An Additive Mixture Model
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
An Approach to Model and Predict the Popularity of Online Contents with Explanatory Factors
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Predicting the Virtual Temperature of Web-Blog Articles as a Measurement Tool for Online Popularity
CIT '11 Proceedings of the 2011 IEEE 11th International Conference on Computer and Information Technology
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It is interesting and informative to predict the set of articles that will be popular from the early observation stage. In this paper, we concentrate on the characteristics of hot articles and estimate the saturation point that is the earliest time the hits variation approaches zero. We have shown the statistical measures of our prediction method for popular articles, by observing the hit records from the birth time of the article. Our experiment showed that the more popular the subject article, the harder it is to identify such popular articles by observing only the early stage. The main contributions of this paper are as follows. We revealed the relationship between the amount of observation data and the predictability of popular articles. And We showed that there is a limit in predicting highly popular articles using partial knowledge. This implies the high popularity of online articles have common basic characteristics of a dynamic system that is hard to predict at the early stage.