Characterizing the life cycle of online news stories using social media reactions

• DOI: 10.1145/2531602.2531623
• Publication Date: 2014-02-07T14:23:08Z
• Authors: Carlos Castillo; Mohammed El-Haddad; Jürgen Pfeffer; Matt Stempeck
• Keywords: Social and Information Networks (cs.SI); FOS: Computer and information sciences; Computer Science - Computers and Society; Physics - Physics and Society; Computers and Society (cs.CY); 0202 electrical engineering, electronic engineering, information engineering; FOS: Physical sciences; Computer Science - Social and Information Networks; Physics and Society (physics.soc-ph); 02 engineering and technology; H.4.m

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.<br/>Computer Supported Cooperative Work and Social Computing (CSCW 2014)<br/>