Characterizing the Life Cycle of Online News StoriesUsing Social Media Reactions
Name
castillo_elhaddad_pfeffer_stempeck_2013_news_lifecycle.pdf
Description
Characterizing the Life Cycle of Online News StoriesUsing Social Media Reactions
Size
557.53 KB
Format
Adobe PDF
Checksum (MD5)
ab7c72d7c2fd30fd21c7df33af977af0
Author(s)
Castillo, Carlos
El-Haddad, Mohammed
Pfeffer, Jürgen
Stempeck, Matt
Date Issued
February 15, 2014
Publisher
CSCW '14: Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
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 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 accuracy of the early prediction of shelf-life for news stories.
Subjects
Web analytics; predictive web analytics; online news; Information Systems Applications
MIT Department
Massachusetts Institute of Technology. Center for Civic Media
Persistent DSpace Link
