The predictive power of online chatter
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Earlier Web usage statistics as predictors of later citation impact: Research Articles
Journal of the American Society for Information Science and Technology
Using a model of social dynamics to predict popularity of news
Proceedings of the 19th international conference on World wide web
Predicting the popularity of online content
Communications of the ACM
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
Patterns of temporal variation in online media
Proceedings of the fourth ACM international conference on Web search and data mining
The tube over time: characterizing popularity growth of youtube videos
Proceedings of the fourth ACM international conference on Web search and data mining
Proceedings of the 20th international conference on World wide web
Toward predicting popularity of social marketing messages
SBP'11 Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction
Predicting the popularity of online articles based on user comments
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
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
Attention prediction on social media brand pages
Proceedings of the 20th ACM international conference on Information and knowledge management
Event analytics via social media
SBNMA '11 Proceedings of the 2011 ACM workshop on Social and behavioural networked media access
A straw shows which way the wind blows: ranking potentially popular items from early votes
Proceedings of the fifth ACM international conference on Web search and data mining
Dynamical classes of collective attention in twitter
Proceedings of the 21st international conference on World Wide Web
Information diffusion and external influence in networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-faceted ranking of news articles using post-read actions
Proceedings of the 21st ACM international conference on Information and knowledge management
Predicting aggregate social activities using continuous-time stochastic process
Proceedings of the 21st ACM international conference on Information and knowledge management
Using early view patterns to predict the popularity of youtube videos
Proceedings of the sixth ACM international conference on Web search and data mining
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
FAST: forecast and analytics of social media and traffic
Proceedings of the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing
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This paper presents a study of the life cycle of news articles posted online. We describe the interplay between website visitation patterns and social media reactions to news content. We show that we can use this hybrid observation method to characterize distinct classes of articles. We also find that social media reactions can help predict future visitation patterns early and accurately. We validate our methods using qualitative analysis as well as quantitative analysis on data from a large international news network, for a set of articles generating more than 3,000,000 visits and 200,000 social media reactions. We show that it is possible to model accurately the overall traffic articles will ultimately receive by observing the first ten to twenty minutes of social media reactions. Achieving the same prediction accuracy with visits alone would require to wait for three hours of data. We also describe significant improvements on the accuracy of the early prediction of shelf-life for news stories.