Characterizing the life cycle of online news stories using social media reactions
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
DOI:
10.1145/2531602.2531623
Publication Date:
2014-02-07T14:23:08Z
AUTHORS (4)
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.<br/>Computer Supported Cooperative Work and Social Computing (CSCW 2014)<br/>
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