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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 mediareactions to news content. We show that we can use this hybrid observationmethod to characterize distinct classes of articles. We also find that socialmedia reactions can help predict future visitation patterns early andaccurately.  We validate our methods using qualitative analysis as well as quantitativeanalysis on data from a large international news network, for a set of articlesgenerating more than 3,000,000 visits and 200,000 social media reactions. Weshow that it is possible to model accurately the overall traffic articles willultimately receive by observing the first ten to twenty minutes of social mediareactions. Achieving the same prediction accuracy with visits alone wouldrequire to wait for three hours of data. We also describe significantimprovements on the accuracy of the early prediction of shelf-life for newsstories.

Characterizing the Life Cycle of Online News Stories Using Social Media Reactions