Characterizing the Life Cycle of Online News StoriesUsing Social Media Reactions
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 als...
| Main Authors: | , , , |
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| Format: | Presentation |
| Language: | English |
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CSCW '14: Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/1721.1/123457 |
| _version_ | 1811097950470799360 |
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| author | Castillo, Carlos El-Haddad, Mohammed Pfeffer, Jürgen Stempeck, Matt |
| author_facet | Castillo, Carlos El-Haddad, Mohammed Pfeffer, Jürgen Stempeck, Matt |
| author_sort | Castillo, Carlos |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T17:07:34Z |
| format | Presentation |
| id | mit-1721.1/123457 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T17:07:34Z |
| publishDate | 2020 |
| publisher | CSCW '14: Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing |
| record_format | dspace |
| spelling | mit-1721.1/1234572020-01-17T03:00:30Z Characterizing the Life Cycle of Online News StoriesUsing Social Media Reactions Castillo, Carlos El-Haddad, Mohammed Pfeffer, Jürgen Stempeck, Matt Web analytics; predictive web analytics; online news; Information Systems Applications 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. 2020-01-16T17:03:43Z 2020-01-16T17:03:43Z 2014-02-15 Presentation https://hdl.handle.net/1721.1/123457 en application/pdf CSCW '14: Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing |
| spellingShingle | Web analytics; predictive web analytics; online news; Information Systems Applications Castillo, Carlos El-Haddad, Mohammed Pfeffer, Jürgen Stempeck, Matt Characterizing the Life Cycle of Online News StoriesUsing Social Media Reactions |
| title | Characterizing the Life Cycle of Online News StoriesUsing Social Media Reactions |
| title_full | Characterizing the Life Cycle of Online News StoriesUsing Social Media Reactions |
| title_fullStr | Characterizing the Life Cycle of Online News StoriesUsing Social Media Reactions |
| title_full_unstemmed | Characterizing the Life Cycle of Online News StoriesUsing Social Media Reactions |
| title_short | Characterizing the Life Cycle of Online News StoriesUsing Social Media Reactions |
| title_sort | characterizing the life cycle of online news storiesusing social media reactions |
| topic | Web analytics; predictive web analytics; online news; Information Systems Applications |
| url | https://hdl.handle.net/1721.1/123457 |
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