Enhancing clustering blog documents by utilizing author/reader comments
ACM-SE 45 Proceedings of the 45th annual southeast regional conference
Description and Prediction of Slashdot Activity
LA-WEB '07 Proceedings of the 2007 Latin American Web Conference
Comments-oriented blog summarization by sentence extraction
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
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
A measurement-driven analysis of information propagation in the flickr social network
Proceedings of the 18th international conference on World wide web
Digging Digg: Comment Mining, Popularity Prediction, and Social Network Analysis
WISM '09 Proceedings of the 2009 International Conference on Web Information Systems and Mining
Predicting the popularity of online content
Communications of the ACM
On the Use of Reservoir Computing in Popularity Prediction
INTERNET '10 Proceedings of the 2010 2nd International Conference on Evolving Internet
Editorial: Special issue on advances in web intelligence
Neurocomputing
A peek into the future: predicting the evolution of popularity in user generated content
Proceedings of the sixth ACM international conference on Web search and data mining
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We propose a general framework which can be used for modeling and predicting the popularity of online contents. The aim of our modeling is not inferring the precise popularity value of a content, but inferring the likelihood with which the content will be popular. Our approach is rooted in survival analysis which deals with the survival time until an event of a failure or death. Survival analysis assumes that predicting the precise lifetime of an instance is very hard but predicting the likelihood of the lifetime of an instance is possible based on its hazard distribution. Additionally we position ourselves in the standpoint of an external observer who has to model the popularity of contents only with publicly available information. Thus, the goal of our proposed methodology is to model a certain popularity metric, such as the lifetime of a content and the number of comments which a content receives, with a set of explanatory factors, which are observable by the external observer. Among various parametric and non-parametric approaches for the survival analysis, we use the Cox proportional hazard regression model, which divides the distribution function of a certain popularity metric into two components: one which is explained by a set of explanatory factors, called risk factors, and another, a baseline survival distribution function, which integrates all the factors not taken into account. In order to validate our proposed methodology, we use two datasets crawled from two different discussion forums, forum.dpreview.com and forums.myspace.com, which are one of the largest discussion forum dealing various issues on digital cameras and a discussion forum provided by a representative social networks. We model two difference popularity metrics, the lifetime of threads and the number of comments, and we show that the models can predict the lifetime of threads from Dpreview (Myspace) by observing a thread during the first 5-6days (24h, respectively) and the number of comments of Dpreview threads by observing a thread during first 2-3days.