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

Carlos Castillo, Mohammed El-Haddad, Jürgen Pfeffer, Matt Stempeck

Research output: Chapter in Book/Report/Conference proceedingConference contribution

78 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationCSCW 2014 - Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing
PublisherAssociation for Computing Machinery
Pages211-223
Number of pages13
ISBN (Print)9781450325400
DOIs
Publication statusPublished - 1 Jan 2014
Event17th ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2014 - Baltimore, MD, United States
Duration: 15 Feb 201419 Feb 2014

Publication series

NameProceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW

Other

Other17th ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2014
CountryUnited States
CityBaltimore, MD
Period15/2/1419/2/14

Keywords

  • Online news
  • Predictive web analytics
  • Web analytics

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Networks and Communications

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  • Cite this

    Castillo, C., El-Haddad, M., Pfeffer, J., & Stempeck, M. (2014). Characterizing the life cycle of online news stories using social media reactions. In CSCW 2014 - Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 211-223). (Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW). Association for Computing Machinery. https://doi.org/10.1145/2531602.2531623