Paper Info
Title
Characterizing the Life Cycle of Online News Stories Using Social Media Reactions
Abstract
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.
Year
DOI
Venue
2014
10.1145/2531602.2531623
CSCW
Keywords
DocType
Volume
web analytics
Conference
abs/1304.3010
Citations 
PageRank 
References 
54
1.91
20
Authors
4
Name
Order
Citations
PageRank
Carlos Castillo15033246.57
Mohammed El-Haddad2662.55
Jürgen Pfeffer334626.57
Matt Stempeck4592.57