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Characterizing the life cycle of online news stories using social media reactions.
| Content Provider | CiteSeerX |
|---|---|
| Author | Castillo, Carlos El-Haddad, Mohammed Jazeera, Al Stempeck, Matt Pfeffer, Jürgen |
| Abstract | This paper presents a study of the life cycle of news articles posted online. We consider user activity both from the perspective of their visitation patterns and from their social media reactions. We show that we can use this information to characterize distinct classes of articles, and that we can use social media reactions to predict future visitation patterns early and accurately. We validate our methods using qualitative analysis as well as quantitative analysis on data from the website of Al Jazeera in English, 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 predict the overall traffic an article will receive with the first ten minutes of social media reactions; the prediction accuracy is equivalent to the one based solely on visits after three hours. We also describe significant improvements on the accuracy of the prediction of shelf-life for news stories. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Social Medium Reaction Life Cycle Prediction Accuracy Significant Improvement Visitation Pattern News Story Overall Traffic Future Visitation Pattern First Ten Minute User Activity Distinct Class Qualitative Analysis Quantitative Analysis News Article |
| Content Type | Text |
| Resource Type | Article |
