A model for popularity dynamics to predict hot articles in discussion blog

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.

[1]  Sung-Hwan Kim,et al.  Predicting the Virtual Temperature of Web-Blog Articles as a Measurement Tool for Online Popularity , 2011, 2011 IEEE 11th International Conference on Computer and Information Technology.

[2]  Philip S. Yu,et al.  Identifying the influential bloggers in a community , 2008, WSDM '08.

[3]  Kavé Salamatian,et al.  An Approach to Model and Predict the Popularity of Online Contents with Explanatory Factors , 2010, 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[4]  Hung-Yu Kao,et al.  Blog Popularity Mining Using Social Interconnection Analysis , 2010, IEEE Internet Computing.

[5]  Vicenç Gómez,et al.  Statistical analysis of the social network and discussion threads in slashdot , 2008, WWW.

[6]  Kristina Lerman,et al.  Social Networks and Social Information Filtering on Digg , 2006, ICWSM.

[7]  Jean-Samuel Beuscart,et al.  Online Social Network Popularity Evolution: An Additive Mixture Model , 2010, 2010 International Conference on Advances in Social Networks Analysis and Mining.

[8]  James Durbin,et al.  Inversions and Rank Correlation Coefficients , 1951 .

[9]  Clu-istos Foutsos,et al.  Fast subsequence matching in time-series databases , 1994, SIGMOD '94.

[10]  Kristina Lerman,et al.  Analysis of social voting patterns on digg , 2008, WOSN '08.

[11]  Huzefa Rangwala,et al.  Digging Digg: Comment Mining, Popularity Prediction, and Social Network Analysis , 2009, 2009 International Conference on Web Information Systems and Mining.

[12]  Bachir Boucheham.  Reduced data similarity-based matching for time series patterns alignment , 2010, Pattern Recognit. Lett..

[13]  Kristina Lerman,et al.  Social Information Processing in News Aggregation , 2007, IEEE Internet Computing.

[14]  Bernardo A. Huberman,et al.  Predicting the popularity of online content , 2008, Commun. ACM.