TY - GEN
T1 - Characterizing the life cycle of online news stories using social media reactions
AU - Castillo, Carlos
AU - El-Haddad, Mohammed
AU - Pfeffer, Jürgen
AU - Stempeck, Matt
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Online news
KW - Predictive web analytics
KW - Web analytics
UR - http://www.scopus.com/inward/record.url?scp=84898948378&partnerID=8YFLogxK
U2 - 10.1145/2531602.2531623
DO - 10.1145/2531602.2531623
M3 - Conference contribution
AN - SCOPUS:84898948378
SN - 9781450325400
T3 - Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW
SP - 211
EP - 223
BT - CSCW 2014 - Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing
PB - Association for Computing Machinery
T2 - 17th ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2014
Y2 - 15 February 2014 through 19 February 2014
ER -