Characterizing the life cycle of online news stories using social media reactions”, ACM conference on Computer supported cooperative work and social computing

This paper presents a study of the life cycle of news ar-ticles 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 ar-ticles. 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 inter-national news network, for a set of articles generating more than 3,000,000 visits and 200,000 social media re-actions. We show that it is possible to model accurately the overall traffic articles will ultimately receive by ob-serving 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 accu-racy of the early prediction of shelf-life for news stories