  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.
