Characterizing the Life Cycle of Online News Stories Using Social Media Reactions

I’ve graduated, but that doesn’t make it any less exciting to see my first academic paper accepted for publication. Thanks to Carlos CastilloMohammed El-Haddad, and Jürgen Pfeffer for driving this paper, and for inviting me to collaborate. Take a look. Al Jazeera English provided us with some great data.

To appear in Computer Supported Cooperative Work and Social Computing. Baltimore, USA. February 2014.

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The shelf-life of hard news vs feature pieces
The shelf-life of hard news vs feature pieces on Al Jazeera English

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 the 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 be used to 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.

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